Search

This manuscript explores the argument for lower student-to-school counselor ratios in U.S. public education. Drawing upon a comprehensive historical review and existing research, we establish the integral role of school counselors and the notable benefits of reduced student-to-counselor ratios. Our analysis of national data exposes marked disparities across states and districts, with the most underfunded often serving higher percentages of low-income students and students of color. This situation raises significant ethical concerns, prompting a call for conscientious policy reform and targeted investment. Informed by emerging best practices, we propose recommendations for enhancing counselor staffing and ultimately student outcomes. This ethical argument underscores the need for proactive actions and provides a basis for future research to further delineate the impact of school counselor ratios on educational equity and student success.

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym -- the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: \textit{de novo} molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at \url{https://github.com/pluskal-lab/MassSpecGym}.

The C-19 star stream has the abundance characteristics of an unusually metal poor globular cluster but kinematically is uncharacteristically hot and wide for a cluster stream, having a line of sight velocity dispersion of 6 \kms\ and a 1-sigma width of 240 pc. We show that the tidal dissolution of an old, lower mass, globular cluster in a CDM galactic halo naturally creates a hot, wide stream currently near orbital apocenter. More generally, simulations show that hot streams, which are all near their orbital apocenter, become thin, cool streams near pericenter. Furthermore, the wide streams from a population of dissolved clusters in the simulations have a mean galactocentric radial velocity dispersion of 7.8$\pm$1.0 \kms\ in a CDM cosmology but only 4.1$\pm$1.6 \kms\ in a WDM (5.5 keV) simulation. A detailed C-19 model in a simplified Milky Way halo potential with a CDM subhalo population provides a lower bound to stream heating, finding that the stream develops a line of sight velocity dispersion of 4.1$\pm$1.1 \kms, whereas WDM (5.5 keV) subhalos give 3.1$\pm$0.1\kms. Known dwarf galaxies alone provide negligible heating. There are five other currently known streams wider than 200 pc that contain a globular cluster, all near their orbital apocenter., Comment: AAS submitted

Red quasars, often associated with potent [OIII] outflows on both galactic and circumgalactic scales, may play a pivotal role in galactic evolution and black hole feedback. In this work, we explore the [FeII] emission in one such quasar at redshift z = 0.4352, F2M J110648.32+480712.3, using the integral field unit (IFU) mode of the Near Infrared Spectrograph (NIRSpec) aboard the James Webb Space Telescope (JWST). Our observations reveal clumpy [FeII] gas located to the south of the quasar. By comparing the kinematics of [FeII] and [OIII], we find that the clumpy [FeII] gas in the southeast and southwest aligns with the outflow, exhibiting similar median velocities up to v_50 ~ 1200 km/s and high velocity widths W_80 > 1000 km/s. In contrast, the [FeII] gas to the south shows kinematics inconsistent with the outflow, with W_80 ~ 500 km/s, significantly smaller than the [OIII] at the same location, suggesting that the [FeII] may be confined within the host galaxy. Utilizing standard emission-line diagnostic ratios, we map the ionization sources of the gas. According to the MAPPINGS III shock models for [FeII]/Pabeta, the regions to the southwest and southeast of the quasar are primarily photoionized. Conversely, the [FeII] emission to the south is likely excited by shocks generated by the back-pressure of the outflow on the galaxy disk, a direct signature of the impact of the quasar on its host.

In this Letter, we investigate the turbulence and energy injection in the extended nebulae surrounding two luminous obscured quasars, WISEA J100211.29$+$013706.7 ($z=1.5933$) and SDSS J165202.64$+$172852.3 ($z=2.9489$). Utilizing high-resolution data from the NIRSpec IFU onboard the James Webb Space Telescope, we analyze the velocity fields of line-emitting gas in and around these quasars and construct the second-order velocity structure functions (VSFs) to quantify turbulent motions across different spatial scales. Our findings reveal a notable flattening in the VSFs from $\approx\!3$ kpc up to a scale of 10--20 kpc, suggesting that energy injection predominantly occurs at a scale $\lesssim$10 kpc, likely powered by quasar outflows and jet-driven bubbles. The extended spatial range of flat VSFs may also indicate the presence of multiple energy injection sources at these scales. For J1652, the turbulent energy in the host interstellar medium (ISM) is significantly higher than in tidally stripped gas, consistent with the expectation of active galactic nucleus (AGN) activities stirring up the host ISM. Compared to the VSFs observed on spatial scales of 10--50 kpc around lower-redshift UV-bright quasars, these obscured quasars exhibit higher turbulent energies in their immediate surroundings, implying different turbulence drivers between the ISM and halo-scale gas. Future studies with an expanded sample are essential to elucidate further the extent and the pivotal role of AGNs in shaping the gas kinematics of host galaxies and beyond., Comment: 10 pages, 4 figures; submitted; comments welcome!

The precise measurement of parity-violating asymmetries in parity-violating electron scattering experiments is a powerful tool for probing new physics beyond the Standard Model. Achieving the expected precision requires both experimental and post-processing signal corrections. This includes using auxiliary detectors to distinguish the main signal from background signals and implementing post-measurement corrections, such as the Bayesian statistics method, to address uncontrolled factors during the experiments. Asymmetry values in the scattering of electrons off proton targets in QWeak and P2 and off electron targets in MOLLER are influenced by detector array configurations, beam polarization angles, and beam spin variations. The Bayesian framework refines full probabilistic models to account for all necessary factors, thereby extracting asymmetry values and the underlying physics under specified conditions. For the QWeak experiment, a reanalysis of the inelastic asymmetry measurement using the Bayesian method has yielded a closer fit to measured asymmetries, with uncertainties reduced by 40\% compared to the Monte Carlo minimization method. This approach was successfully applied to simulated data for the MOLLER experiment and is predicted to be similarly effective in P2., Comment: 10 pages, 5 figures, submitted to Physical Review C

Quasar feedback may play a key role in the evolution of massive galaxies. The dust-reddened quasar, F2M110648.35$+$480712 at $z = 0.4352$ is one of the few cases at its redshift that exhibits powerful quasar feedback through bipolar outflows. Our new observation with the integral field unit mode of Near-infrared Spectrograph onboard JWST opens a new window to examine this spectacular outflow through Pa$\alpha$ emission line with $\sim$3$\times$ better spatial resolution than previous work. The morphology and kinematics of the Pa$\alpha$ nebula confirm the existence of a bipolar outflow extending on a scale of $\sim$17$\times$14 kpc and with a velocity reaching $\sim$1100 km s$^{-1}$. The higher spatial resolution of our new observation leads to more reliable measurements of outflow kinematics. Considering only the spatially resolved outflow and assuming an electron density of 100 cm$^{-2}$, the mass, momentum and kinetic energy outflow rates are $\sim$50-210 M$_{\odot}$ yr$^{-1}$, $\sim$0.3-1.7$\times$10$^{36}$ dynes ($\sim$14-78\% of the quasar photon momentum flux) and $\sim$0.16-1.27$\times$10$^{44}$ erg s$^{-1}$ ($\sim$0.02-0.20\% of the quasar bolometric luminosity), respectively. The local instantaneous outflow rates generally decrease radially. We infer that the quasar is powerful enough to drive the outflow, while stellar processes cannot be overlooked as a contributing energy source. The mass outflow rate is $\sim$0.4-1.5 times the star formation rate, and the ratio of kinetic energy outflow rate to the quasar bolometric luminosity is comparable to the minimum value required for negative quasar feedback in simulations. This outflow may help regulate the star formation activity within the system to some extent., Comment: 14 pages, 6 figures, 1 table, ApJ in review

Rydberg states of alkali atoms are highly sensitive to electromagnetic radiation in the GHz-to-THz regime because their transitions have large electric dipole moments. Consequently, environmental blackbody radiation (BBR) can couple Rydberg states together at $\mu$s timescales. Here, we track the BBR-induced transfer of a prepared Rydberg state to its neighbors and use the evolution of these state populations to characterize the BBR field at the relevant wavelengths, primarily at 130 GHz. We use selective field ionization readout of Rydberg states with principal quantum number $n\sim30$ in $^{85}$Rb and substantiate our ionization signal with a theoretical model. With this detection method, we measure the associated blackbody-radiation-induced time dynamics of these states, reproduce the results with a simple semi-classical population transfer model, and demonstrate that this measurement is temperature sensitive with a statistical sensitivity to the fractional temperature uncertainty of 0.09 Hz$^{-1/2}$, corresponding to 26 K$\cdot$Hz$^{-1/2}$ at room temperature. This represents a calibration-free SI-traceable temperature measurement, for which we calculate a systematic fractional temperature uncertainty of 0.006, corresponding to 2 K at room temperature when used as a primary temperature standard.

The Central Molecular Zone (CMZ) of the Milky Way is fed by gas inflows from the Galactic disk along almost radial trajectories aligned with the major axis of the Galactic bar. However, despite being fundamental to all processes in the nucleus of the galaxy, these inflows have been studied significantly less than the CMZ itself. We present observations of various molecular lines between 215 and 230 GHz for 20 clouds with $|\ell| < 10^\circ$, which are candidates for clouds in the Galactic bar due to their warm temperatures and broad lines relative to typical Galactic disk clouds, using the Atacama Large Millimeter/submillimeter Array (ALMA) Atacama Compact Array (ACA). We measure gas temperatures, shocks, star formation rates, turbulent Mach numbers, and masses for these clouds. Although some clouds may be in the Galactic disk despite their atypical properties, nine clouds are likely associated with regions in the Galactic bar, and in these clouds, turbulent pressure is suppressing star formation. In clouds with no detected star formation, turbulence is the dominant heating mechanism, whereas photo-electric processes heat the star-forming clouds. We find that the ammonia (NH3) and formaldehyde (H2CO) temperatures probe different gas components, and in general each transition appears to trace different molecular gas phases within the clouds. We also measure the CO-to-H2 X-factor in the bar to be an order of magnitude lower than the typical Galactic value. These observations provide evidence that molecular clouds achieve CMZ-like properties before reaching the CMZ, Comment: 43 pages, 22 figures. Accepted to ApJ

Humanitarian organizations can enhance their effectiveness by analyzing data to discover trends, gather aggregated insights, manage their security risks, support decision-making, and inform advocacy and funding proposals. However, data about violent incidents with direct impact and relevance for humanitarian aid operations is not readily available. An automatic data collection and NLP-backed classification framework aligned with humanitarian perspectives can help bridge this gap. In this paper, we present HumVI - a dataset comprising news articles in three languages (English, French, Arabic) containing instances of different types of violent incidents categorized by the humanitarian sector they impact, e.g., aid security, education, food security, health, and protection. Reliable labels were obtained for the dataset by partnering with a data-backed humanitarian organization, Insecurity Insight. We provide multiple benchmarks for the dataset, employing various deep learning architectures and techniques, including data augmentation and mask loss, to address different task-related challenges, e.g., domain expansion. The dataset is publicly available at https://github.com/dataminr-ai/humvi-dataset.

The morphology of quantum dots plays an important role in governing their photophysics. Here, we explore the photoluminescence of spheroidal CsPbBr3 quantum dots synthesized via the room-temperature trioctlyphosphine oxide/PbBr2 method. Despite photoluminescence quantum yields nearing 100%, these spheroidal quantum dots exhibit an elongated red photoluminescence tail not observed in typical cubic quantum dots synthesized via hot injection. We explore this elongated red tail through structural and optical characterization including small-angle x-ray scattering, transmission electron microscopy and time-resolved, steady-state, and single quantum dot photoluminescence. From these measurements we conclude that the red tail originates from emissive surface traps. We hypothesize that these emissive surface traps are located on the (111) surfaces and show that the traps can be passivated by adding phenethyl ammonium bromide, resulting in a more symmetric line shape

We study the geometry of the Gromoll-Meyer sphere, one of Milnor's exotic $7$-spheres. We focus on a Kaluza-Klein Ansatz, with a round $S^4$ as base space, unit $S^3$ as fibre, and $k=1,2$ $SU(2)$ instantons as gauge fields, where all quantities admit an elegant description in quaternionic language. The metric's moduli space coincides with the $k=1,2$ instantons' moduli space quotiented by the isometry of the base, plus an additional $\mathbb{R}^+$ factor corresponding to the radius of the base, $r$. We identify a "center" of the $k=2$ instanton moduli space with enhanced symmetry. This $k=2$ solution is used together with the maximally symmetric $k=1$ solution to obtain a metric of maximal isometry, $SO(3)\times O(2)$, and to compute its Ricci tensor explicitly. This allows us to put a bound on $r$ to ensure positive Ricci curvature, which implies various energy conditions for an $8$-dimensional static space-time. The construction opens for a concrete examination of the properties of the sectional curvature., Comment: 33 pages, 4 figures

This proceedings contains abstracts and position papers for the work presented at the third Logic and Practice of Programming (LPOP) Workshop. The workshop was held online, using zoom, at stonybrook.zoom.us, on December 13, 2022. The workshop focused on core high-level abstractions around sets and logic rules, to help bring them to the general practice of programming.

Fast accreting, extremely luminous quasars contribute heavily to the feedback process within galaxies. While these systems are most common at cosmic noon ($z\sim2$), here we choose to study PDS 456, an extremely luminous ($L_{bol}\sim 10^{47}$ erg s$^{-1}$) but nearby ($z\sim0.185$) quasar where the physics of feedback can be studied in greater detail. We present the results from our analysis of the JWST MIRI/MRS integral field spectroscopic (IFS) data of this object. The extreme brightness of PDS 456 makes it challenging to study the extended emission even in this nearby object. MIRI/MRS instrumental effects are mitigated by using complementary NIRSpec and MUSE IFS data cubes. We show clear evidence of a multiphase gas outflow extending up to 15 kpc from the central source. This includes emission from warm molecular (H$_2$ $\nu$ = 0 $-$ 0 and 1 $-$ 0) and ionized (e.g. Pa$\alpha$, [O III], [Ne III], [Ne VI]) gas with typical blueshifted velocities down to $-500$ km s$^{-1}$. We are also able to probe the nuclear dust emission in this source through silicate and PAH emission features but are unable to spatially resolve it. Our results are consistent with this powerful quasar driving a radiatively driven wind over a broad range of distances and altering the ionization structure of the host galaxy., Comment: 26 pages, 15 figures

We use a neural network model and ALMA observations of HCN and HNC to constrain the physical conditions, most notably the cosmic-ray ionization rate (CRIR, zeta), in the Central Molecular Zone (CMZ) of the starburst galaxy NGC 253. Using output from the chemical code UCLCHEM, we train a neural network model to emulate UCLCHEM and derive HCN and HNC molecular abundances from a given set of physical conditions. We combine the neural network with radiative transfer modeling to generate modeled integrated intensities, which we compare to measurements of HCN and HNC from the ALMA Large Program ALCHEMI. Using a Bayesian nested sampling framework, we constrain the CRIR, molecular gas volume and column densities, kinetic temperature, and beam-filling factor across NGC 253's CMZ. The neural network model successfully recovers UCLCHEM molecular abundances with about 3 percent error and, when used with our Bayesian inference algorithm, increases the parameter inference speed tenfold. We create images of these physical parameters across NGC 253's CMZ at 50 pc resolution and find that the CRIR, in addition to the other gas parameters, is spatially variable with zeta a few times 10^{14} s^{-1} at greater than 100 pc from the nucleus, increasing to zeta greater than 10^{-13} s^{-1} at its center. These inferred CRIRs are consistent within 1 dex with theoretical predictions based on non-thermal emission. Additionally, the high CRIRs estimated in NGC 253's CMZ can be explained by the large number of cosmic-ray-producing sources as well as a potential suppression of cosmic-ray diffusion near their injection sites.

High-velocity outflows are ubiquitous in compact, massive (M$_* \sim$ 10$^{11}$ M$_{\odot}$), z $\sim$ 0.5 galaxies with extreme star formation surface densities ($\Sigma_{SFR} \sim$ 2000 M$_{\odot}$ yr$^{-1}$ kpc$^{-2}$). We have previously detected and characterized these outflows using MgII absorption lines. To probe their full extent, we present Keck/KCWI integral field spectroscopy of the [OII] and MgII emission nebulae surrounding all of the 12 galaxies in this study. We find that [OII] is more effective than MgII in tracing low surface brightness, extended emission in these galaxies. The [OII] nebulae are spatially extended beyond the stars, with radial extent R$_{90}$ between 10 and 40 kpc. The nebulae exhibit non-gravitational motions, indicating galactic outflows with maximum blueshifted velocities ranging from -335 to -1920 km s$^{-1}$. The outflow kinematics correlate with the bursty star formation histories of these galaxies. Galaxies with the most recent bursts of star formation (within the last $<$ 3 Myr) exhibit the highest central velocity dispersions ($\sigma >$ 400 km s$^{-1}$), while the oldest bursts have the lowest-velocity outflows. Many galaxies exhibit both high-velocity cores and more extended, slower-moving gas indicative of multiple outflow episodes. The slower, larger outflows occurred earlier and have decelerated as they propagate into the CGM and mix on timescales $>$ 50 Myr., Comment: Accepted for publication in The Astrophysical Journal

We demonstrate the use of [2-($\textit{9H}$-carbazol-9-yl)ethyl]phosphonic acid (2PACz) and [2-(3,6-di-$\textit{tert}$-butyl-$\textit{9H}$-carbazol-9-yl)ethyl]phosphonic acid (t-Bu-2PACz) as anode modification layers in metal-halide perovskite quantum dot light-emitting diodes (QLEDs). Compared to conventional QLED structures with PEDOT:PSS (poly(3,4-ethylenedioxythiophene) polystyrene sulfonate)/PVK (poly(9-vinylcarbazole)) hole-transport layers, QLEDs made with phosphonic acid (PA)-modified indium tin oxide (ITO) anodes show an over 7-fold increase in brightness, achieving a brightness of 373,000 cd m$^{-2}$, one of the highest brightnesses reported to date for colloidal perovskite QLEDs. Importantly, the onset of efficiency roll-off, or efficiency droop, occurs at ~1000-fold higher current density for QLEDs made with PA-modified anodes compared to control QLEDs made with conventional PEDOT:PSS/PVK hole transport layers, allowing the devices to sustain significantly higher levels of external quantum efficiency at a brightness of >10$^{5}$ cd m$^{-2}$. Steady-state and time-resolved photoluminescence measurements indicate these improvements are due to a combination of multiple factors, including reducing quenching of photoluminescence at the PEDOT:PSS interface and reducing photoluminescence efficiency loss at high levels of current density.

Global disparities in neurosurgical care necessitate innovations addressing affordability and accuracy, particularly for critical procedures like ventriculostomy. This intervention, vital for managing life-threatening intracranial pressure increases, is associated with catheter misplacement rates exceeding 30% when using a freehand technique. Such misplacements hold severe consequences including haemorrhage, infection, prolonged hospital stays, and even morbidity and mortality. To address this issue, we present a novel, stand-alone mobile-based augmented reality system (iSurgARy) aimed at significantly improving ventriculostomy accuracy, particularly in resource-limited settings such as those in low- and middle-income countries. iSurgARy uses landmark based registration by taking advantage of Light Detection and Ranging (LiDaR) to allow for accurate surgical guidance. To evaluate iSurgARy, we conducted a two-phase user study. Initially, we assessed usability and learnability with novice participants using the System Usability Scale (SUS), incorporating their feedback to refine the application. In the second phase, we engaged human-computer interaction (HCI) and clinical domain experts to evaluate our application, measuring Root Mean Square Error (RMSE), System Usability Scale (SUS) and NASA Task Load Index (TLX) metrics to assess accuracy usability, and cognitive workload, respectively

This paper introduces a novel approach to personalised federated learning within the $\mathcal{X}$-armed bandit framework, addressing the challenge of optimising both local and global objectives in a highly heterogeneous environment. Our method employs a surrogate objective function that combines individual client preferences with aggregated global knowledge, allowing for a flexible trade-off between personalisation and collective learning. We propose a phase-based elimination algorithm that achieves sublinear regret with logarithmic communication overhead, making it well-suited for federated settings. Theoretical analysis and empirical evaluations demonstrate the effectiveness of our approach compared to existing methods. Potential applications of this work span various domains, including healthcare, smart home devices, and e-commerce, where balancing personalisation with global insights is crucial.

Fitting nonlinear dynamical models to sparse and noisy observations is fundamentally challenging. Identifying dynamics requires data assimilation (DA) to estimate system states, but DA requires an accurate dynamical model. To break this deadlock we present CODA, an end-to-end optimization scheme for jointly learning dynamics and DA directly from sparse and noisy observations. A neural network is trained to carry out data accurate, efficient and parallel-in-time DA, while free parameters of the dynamical system are simultaneously optimized. We carry out end-to-end learning directly on observation data, introducing a novel learning objective that combines unrolled auto-regressive dynamics with the data- and self-consistency terms of weak-constraint 4Dvar DA. By taking into account interactions between new and existing simulation components over multiple time steps, CODA can recover initial conditions, fit unknown dynamical parameters and learn neural network-based PDE terms to match both available observations and self-consistency constraints. In addition to facilitating end-to-end learning of dynamics and providing fast, amortized, non-sequential DA, CODA provides greater robustness to model misspecification than classical DA approaches., Comment: Submitted to Journal of Advances in Modeling Earth Systems (JAMES)

Background: Limited-angle (LA) dual-energy (DE) cone-beam CT (CBCT) is considered as a potential solution to achieve fast and low-dose DE imaging on current CBCT scanners without hardware modification. However, its clinical implementations are hindered by the challenging image reconstruction from LA projections. While optimization-based and deep learning-based methods have been proposed for image reconstruction, their utilization is limited by the requirement for X-ray spectra measurement or paired datasets for model training. Purpose: This work aims to facilitate the clinical applications of fast and low-dose DECBCT by developing a practical solution for image reconstruction in LA-DECBCT. Methods: An inter-spectral structural similarity-based regularization was integrated into the iterative image reconstruction in LA-DECBCT. By enforcing the similarity between the DE images, LA artifacts were efficiently reduced in the reconstructed DECBCT images. The proposed method was evaluated using four physical phantoms and three digital phantoms, demonstrating its efficacy in quantitative DECBCT imaging. Results: In all the studies, the proposed method achieves accurate image reconstruction without visible residual artifacts from LA-DECBCT projection data. In the digital phantom study, the proposed method reduces the mean-absolute-error (MAE) from 419 to 14 HU for the High-energy CBCT and 591 to 20 HU for the low-energy CBCT. Conclusions: The proposed method achieves accurate image reconstruction without the need for X-ray spectra measurement for optimization or paired datasets for model training, showing great practical value in clinical implementations of LA-DECBCT.

We investigate the phenomenon of gravitational baryogenesis within the context of a specific modified theory of gravity, namely, energy-momentum squared gravity or $f(R, T_{\mu\nu}T^{\mu\nu})$ gravity. In this framework, the gravitational Lagrangian is formulated as a general function of the Ricci scalar $R$ and the self-contraction of the energy-momentum tensor, $\mathcal{T}^2 \equiv T_{\mu\nu}T^{\mu\nu}$. This approach extends the conventional paradigm of gravitational baryogenesis by introducing new dependencies that allow for a more comprehensive exploration of the baryon asymmetry problem. Our analysis aims to elucidate the role of these gravitational modifications in the generation of baryon asymmetry, a critical issue in cosmology that remains unresolved within the Standard Model of particle physics. By incorporating $\mathcal{T}^2$ into the gravitational action, we propose that these modifications can significantly influence the dynamics of the early universe, thereby altering the conditions under which baryogenesis occurs. This study not only provides a novel depiction of gravitational baryogenesis but also offers insights into how modified gravity theories can address the longstanding question of baryon asymmetry. The implications of our findings suggest that $f(R, T_{\mu\nu}T^{\mu\nu})$ gravity could play a crucial role in understanding the fundamental processes that led to the matter-antimatter imbalance observed in the universe today., Comment: 22 pages, 5 figures

We present a low-resource and robust optical implementation of the four-dimensional Grover coin, a four-port linear-optical scatterer that augments the low dimensionality of a regular beam-splitter. While prior realizations of the Grover coin required a potentially unstable ring-cavity to be formed, this version of the scatterer does not exhibit any internal interference. When this Grover coin is placed in another system, it can be used for interferometry with a higher-dimensional set of optical field modes. In this case, we formed a Grover-Michelson interferometer, which results when the traditional beam-splitter of a Michelson interferometer is replaced with a four-port Grover coin. This replacement has been shown to remove a phase parameter redundancy in the original Michelson system, now allowing continuous tuning of the shape and slope of the interference pattern. We observed an intensity interferogram with $97\%$ visibility and a phase sensitivity more than an order of magnitude larger than a regular Michelson interferometer. Because this device is readily formed with nearly the same number of optomechanical resources as a Michelson interferometer, but can outperform it drastically in phase delay evaluation, it has a great potential to improve many interferometric sensing and control systems., Comment: 12 pages, 6 figures

Accurate musculoskeletal soft tissue tumor segmentation is vital for assessing tumor size, location, diagnosis, and response to treatment, thereby influencing patient outcomes. However, segmentation of these tumors requires clinical expertise, and an automated segmentation model would save valuable time for both clinician and patient. Training an automatic model requires a large dataset of annotated images. In this work, we describe the collection of an MR imaging dataset of 199 musculoskeletal soft tissue tumors from 199 patients. We trained segmentation models on this dataset and then benchmarked them on a publicly available dataset. Our model achieved the state-of-the-art dice score of 0.79 out of the box without any fine tuning, which shows the diversity and utility of our curated dataset. We analyzed the model predictions and found that its performance suffered on fibrous and vascular tumors due to their diverse anatomical location, size, and intensity heterogeneity. The code and models are available in the following github repository, https://github.com/Reasat/mstt, Comment: Dataset will be made publicly available after the acceptance of the paper

Objective: The validity of objective measures derived from high-speed videoendoscopy (HSV) depends, among other factors, on the validity of spatial segmentation. Evaluation of the validity of spatial segmentation requires the existence of reliable ground truths. This study presents a framework for creating reliable ground truth with sub-pixel resolution and then evaluates its performance. Method: The proposed framework is a three-stage process. First, three laryngeal imaging experts performed the spatial segmentation task. Second, regions with high discrepancies between experts were determined and then overlaid onto the segmentation outcomes of each expert. The marked HSV frames from each expert were randomly assigned to the two remaining experts, and they were tasked to make proper adjustments and modifications to the initial segmentation within disparity regions. Third, the outcomes of this reconciliation phase were analyzed again and regions with continued high discrepancies were identified and adjusted based on the consensus among the three experts. This three-stage framework was tested using a custom graphical user interface that allowed precise piece-wise linear segmentation of the vocal fold edges. Inter-rate reliability of segmentation was evaluated using 12 HSV recordings. 10% of the frames from each HSV file were randomly selected to assess the intra-rater reliability. Result and conclusion: The reliability of spatial segmentation progressively improved as it went through the three stages of the framework. The proposed framework generated highly reliable and valid ground truths for evaluating the validity of automated spatial segmentation methods.

Objective: Gadolinium-based contrast agents (GBCAs) are commonly used in MRI scans of patients with gliomas to enhance brain tumor characterization using T1-weighted (T1W) MRI. However, there is growing concern about GBCA toxicity. This study develops a deep-learning framework to generate T1-postcontrast (T1C) from pre-contrast multiparametric MRI. Approach: We propose the tumor-aware vision transformer (TA-ViT) model that predicts high-quality T1C images. The predicted tumor region is significantly improved (P < .001) by conditioning the transformer layers from predicted segmentation maps through adaptive layer norm zero mechanism. The predicted segmentation maps were generated with the multi-parametric residual (MPR) ViT model and transformed into a latent space to produce compressed, feature-rich representations. The TA-ViT model predicted T1C MRI images of 501 glioma cases. Selected patients were split into training (N=400), validation (N=50), and test (N=51) sets. Main Results: Both qualitative and quantitative results demonstrate that the TA-ViT model performs superior against the benchmark MRP-ViT model. Our method produces synthetic T1C MRI with high soft tissue contrast and more accurately reconstructs both the tumor and whole brain volumes. The synthesized T1C images achieved remarkable improvements in both tumor and healthy tissue regions compared to the MRP-ViT model. For healthy tissue and tumor regions, the results were as follows: NMSE: 8.53 +/- 4.61E-4; PSNR: 31.2 +/- 2.2; NCC: 0.908 +/- .041 and NMSE: 1.22 +/- 1.27E-4, PSNR: 41.3 +/- 4.7, and NCC: 0.879 +/- 0.042, respectively. Significance: The proposed method generates synthetic T1C images that closely resemble real T1C images. Future development and application of this approach may enable contrast-agent-free MRI for brain tumor patients, eliminating the risk of GBCA toxicity and simplifying the MRI scan protocol., Comment: arXiv admin note: text overlap with arXiv:2407.02616

The considerable body of data available for evaluating biometric recognition systems in Research and Development (R\&D) environments has contributed to the increasingly common problem of target performance mismatch. Biometric algorithms are frequently tested against data that may not reflect the real world applications they target. From a Testing and Evaluation (T\&E) standpoint, this domain mismatch causes difficulty assessing when improvements in State-of-the-Art (SOTA) research actually translate to improved applied outcomes. This problem can be addressed with thoughtful preparation of data and experimental methods to reflect specific use-cases and scenarios. To that end, this paper evaluates research solutions for identifying individuals at ranges and altitudes, which could support various application areas such as counterterrorism, protection of critical infrastructure facilities, military force protection, and border security. We address challenges including image quality issues and reliance on face recognition as the sole biometric modality. By fusing face and body features, we propose developing robust biometric systems for effective long-range identification from both the ground and steep pitch angles. Preliminary results show promising progress in whole-body recognition. This paper presents these early findings and discusses potential future directions for advancing long-range biometric identification systems based on mission-driven metrics.

This paper examines covariate effects on fused whole body biometrics performance in the IARPA BRIAR dataset, specifically focusing on UAV platforms, elevated positions, and distances up to 1000 meters. The dataset includes outdoor videos compared with indoor images and controlled gait recordings. Normalized raw fusion scores relate directly to predicted false accept rates (FAR), offering an intuitive means for interpreting model results. A linear model is developed to predict biometric algorithm scores, analyzing their performance to identify the most influential covariates on accuracy at altitude and range. Weather factors like temperature, wind speed, solar loading, and turbulence are also investigated in this analysis. The study found that resolution and camera distance best predicted accuracy and findings can guide future research and development efforts in long-range/elevated/UAV biometrics and support the creation of more reliable and robust systems for national security and other critical domains.

Simulating mantle convection often requires reaching a computationally expensive steady-state, crucial for deriving scaling laws for thermal and dynamical flow properties and benchmarking numerical solutions. The strong temperature dependence of the rheology of mantle rocks causes viscosity variations of several orders of magnitude, leading to a slow-evolving stagnant lid where heat conduction dominates, overlying a rapidly-evolving and strongly convecting region. Time-stepping methods, while effective for fluids with constant viscosity, are hindered by the Courant criterion, which restricts the time step based on the system's maximum velocity and grid size. Consequently, achieving steady-state requires a large number of time steps due to the disparate time scales governing the stagnant and convecting regions. We present a concept for accelerating mantle convection simulations using machine learning. We generate a dataset of 128 two-dimensional simulations with mixed basal and internal heating, and pressure- and temperature-dependent viscosity. We train a feedforward neural network on 97 simulations to predict steady-state temperature profiles. These can then be used to initialize numerical time stepping methods for different simulation parameters. Compared to typical initializations, the number of time steps required to reach steady-state is reduced by a median factor of 3.75. The benefit of this method lies in requiring very few simulations to train on, providing a solution with no prediction error as we initialize a numerical method, and posing minimal computational overhead at inference time. We demonstrate the effectiveness of our approach and discuss the potential implications for accelerated simulations for advancing mantle convection research.

Using symmetry considerations, we derive a unitary scattering matrix for a three-port optical Y-coupler or Y-branch. The result is shown to be unique up to external phase shifts. Unlike traditional passive linear-optical one-way splitters, coupling light into the conventional output ports of the Y-coupler results in strong coherent back-reflections, making the device a hybrid between feed-forward devices like the beam splitter, which do not reverse the direction of light, and a recently considered class of directionally unbiased multiport scatterers (with dimension greater than two) which do. While the device could immediately find use as a novel scattering vertex for the implementation of quantum walks, we also design a few simple but nonetheless useful optical systems that can be constructed by taking advantage of the symmetry of the scattering process. This includes an interference-free, resource-efficient implementation of the Grover four-port and a higher-dimensional Fabry-Perot interferometer with tunable finesse. Symmetry-breaking generalizations are also considered.

We introduce a novel Bayesian framework for estimating time-varying volatility by extending the Random Walk Stochastic Volatility (RWSV) model with a new Dynamic Shrinkage Process (DSP) in (log) variances. Unlike classical Stochastic Volatility or GARCH-type models with restrictive parametric stationarity assumptions, our proposed Adaptive Stochastic Volatility (ASV) model provides smooth yet dynamically adaptive estimates of evolving volatility and its uncertainty (vol of vol). We derive the theoretical properties of the proposed global-local shrinkage prior. Through simulation studies, we demonstrate that ASV exhibits remarkable misspecification resilience with low prediction error across various data generating scenarios in simulation. Furthermore, ASV's capacity to yield locally smooth and interpretable estimates facilitates a clearer understanding of underlying patterns and trends in volatility. Additionally, we propose and illustrate an extension for Bayesian Trend Filtering simultaneously in both mean and variance. Finally, we show that this attribute makes ASV a robust tool applicable across a wide range of disciplines, including in finance, environmental science, epidemiology, and medicine, among others.

Many molecular alterations serve as clinically prognostic or therapy-predictive biomarkers, typically detected using single or multi-gene molecular assays. However, these assays are expensive, tissue destructive and often take weeks to complete. Using AI on routine H&E WSIs offers a fast and economical approach to screen for multiple molecular biomarkers. We present a high-throughput AI-based system leveraging Virchow2, a foundation model pre-trained on 3 million slides, to interrogate genomic features previously determined by an next-generation sequencing (NGS) assay, using 47,960 scanned hematoxylin and eosin (H&E) whole slide images (WSIs) from 38,984 cancer patients. Unlike traditional methods that train individual models for each biomarker or cancer type, our system employs a unified model to simultaneously predict a wide range of clinically relevant molecular biomarkers across cancer types. By training the network to replicate the MSK-IMPACT targeted biomarker panel of 505 genes, it identified 80 high performing biomarkers with a mean AU-ROC of 0.89 in 15 most common cancer types. In addition, 40 biomarkers demonstrated strong associations with specific cancer histologic subtypes. Furthermore, 58 biomarkers were associated with targets frequently assayed clinically for therapy selection and response prediction. The model can also predict the activity of five canonical signaling pathways, identify defects in DNA repair mechanisms, and predict genomic instability measured by tumor mutation burden, microsatellite instability (MSI), and chromosomal instability (CIN). The proposed model can offer potential to guide therapy selection, improve treatment efficacy, accelerate patient screening for clinical trials and provoke the interrogation of new therapeutic targets.

By coupling a semiconductor-based planar Josephson junction to a superconducting resonator, we investigate the Andreev bound states in the junction using dispersive readout techniques. Using electrostatic gating to create a narrow constriction in the junction, our measurements unveil a strong coupling interaction between the resonator and the Andreev bound states. This enables the mapping of isolated tunable Andreev bound states, with an observed transparency of up to 99.94\% along with an average induced superconducting gap of $\sim 150 \mu$eV. Exploring the gate parameter space further elucidates a non-monotonic evolution of multiple Andreev bound states with varying gate voltage. Complimentary tight-binding calculations of an Al-InAs planar Josephson junction with strong Rashba spin-orbit coupling provide insight into possible mechanisms responsible for such behavior. Our findings highlight the subtleties of the Andreev spectrum of Josephson junctions fabricated on superconductor-semiconductor heterostructures and offering potential applications in probing topological states in these hybrid platforms.

This paper describes a semantics for pure Prolog programs with negation that provides meaning to metaprograms. Metaprograms are programs that construct and use data structures as programs. In Prolog a primary mataprogramming construct is the use of a variable as a literal in the body of a clause. The traditional Prolog 3-line metainterpreter is another example of a metaprogram. The account given here also supplies a meaning for clauses that have a variable as head, even though most Prolog systems do not support such clauses. This semantics naturally includes such programs, giving them their intuitive meaning. Ideas from M. Denecker and his colleagues form the basis of this approach. The key idea is to notice that if we give meanings to all propositional programs and treat Prolog rules with variables as the set of their ground instances, then we can give meanings to all programs. We must treat Prolog rules (which may be metarules) as templates for generating ground propositional rules, and not as first-order formulas, which they may not be. We use parameterized inductive definitions to give propositional models to Prolog programs, in which the propositions are expressions. Then the set of expressions of a propositional model determine a first-order Herbrand Model, providing a first-order logical semantics for all (pure) Prolog programs, including metaprograms. We give examples to show the applicability of this theory. We also demonstrate how this theory makes proofs of some important properties of metaprograms very straightforward., Comment: 16 pages, 3 figures, appeared in the International Conference on Logic Programming 2024 (ICLP-2024)

Fast-Fourier Transform (FFT) methods have been widely used in solid mechanics to address complex homogenization problems. However, current FFT-based methods face challenges that limit their applicability to intricate material models or complex mechanical problems. These challenges include the manual implementation of constitutive laws and the use of computationally expensive and complex algorithms to couple microscale mechanisms to macroscale material behavior. Here, we incorporate automatic differentiation (AD) within the FFT framework to mitigate these challenges. We demonstrate that AD-enhanced FFT-based methods can derive stress and tangent stiffness directly from energy density functionals, facilitating the extension of FFT-based methods to more intricate material models. Additionally, automatic differentiation simplifies the calculation of homogenized tangent stiffness for microstructures with complex architectures and constitutive properties. This enhancement renders current FFT-based methods more modular, enabling them to tackle homogenization in complex multiscale systems, especially those involving multiphysics processes. Furthermore, we illustrate the use of the AD-enhanced FFT method for problems that extend beyond homogenization, such as uncertainty quantification and topology optimization where automatic differentiation simplifies the computation of sensitivities. Our work will simplify the numerical implementation of FFT-based methods for complex solid mechanics problems., Comment: Updated the article based on the feedback. Added new Sections and new Results

We present a near infrared (NIR) candidate star cluster catalog for the central kiloparsec of M82 based on new JWST NIRCam images. We identify star cluster candidates using the F250M filter, finding 1357 star cluster candidates with stellar masses $>10^4$ M$_\odot$. Compared to previous optical catalogs, nearly all (87%) of the candidates we identify are new. The star cluster candidates have a median intrinsic cluster radius of $\approx$1 pc and have stellar masses up to $10^6$ M$_\odot$. By comparing the color-color diagram to dust-free yggdrasil stellar population models, we estimate that the star cluster candidates have A$_{\rm V}\sim3-24$ mag, corresponding to A$_{\rm 2.5\mu m}\sim0.3-2.1$ mag. There is still appreciable dust extinction towards these clusters into the NIR. We measure the stellar masses of the star cluster candidates, assuming ages of 0 and 8 Myr. The slope of the resulting cluster mass function is $\beta=1.9\pm0.2$, in excellent agreement with studies of star clusters in other galaxies., Comment: Resubmitted to ApJL

The Su-Schrieffer-Heeger (SSH) system is a popular model for exploring topological insulators and topological phases in one dimension. Recent interest in exceptional points has led to re-examination of non-Hermitian generalizations of many physical models, including the SSH model. In such non-Hermitian systems, singular points called exceptional points (EPs) appear that are of interest for applications in super-resolution sensing systems and topological lasers. Here, a non-Hermitian and non-PT-symmetric variation of the SSH model is introduced, in which the hopping amplitudes are non-reciprocal and vary monotonically along the chain. It is found that, while the existence of the EPs is due to the nonreciprocal couplings, the number, position, and order of the EPs can all be altered by the addition of the hopping amplitude gradient, adding a new tool for tailoring the spectrum of a non-Hermitian system.

The search for atmospheric biosignatures in Earth-like exoplanets is one of the most pressing challenges in observational astrobiology. Detecting biogenic gases in terrestrial planets requires high resolution and long integration times. In this work, we developed and tested a machine-learning general methodology, intended to classify transmission spectra with low Signal-to-Noise Ratio according to their potential to contain biosignatures. For that purpose, we trained a set of models capable of classifying noisy transmission spectra, as having methane, ozone, and/or water (multilabel classification), or simply as being interesting for follow-up observations (binary classification). The models were trained with $\sim\, 10^6$ synthetic spectra of planets similar to TRAPPIST-1 e, which were generated with the package MultiREx, especially developed for this work. The trained algorithms correctly classified test planets with transmission spectra having SNR $<$ 6 and containing methane and/or ozone at mixing ratios similar to those of modern and Proterozoic Earth. Tests on realistic synthetic spectra based on the current Earth\'s atmosphere show at least one of our models would classify as likely having biosignatures and using only one transit, most of the inhabited terrestrial planets observed with the JWST/NIRSpec PRISM around M-dwarfs located at distances similar or smaller than that of TRAPPIST-1 e. The implication of this result for the designing of observing programs and future surveys is enormous since machine-assisted strategies similar to those presented here could significantly optimize the usage of JWST resources for biosignature searching, while maximizing the chances of a real discovery after dedicated follow-up observations of promising candidates., Comment: 21 pages, under review in MNRAS, 18 figures, MultiREx package available at https://github.com/D4san/MultiREx-public

Accurately modeling the mechanical behavior of materials is crucial for numerous engineering applications. The quality of these models depends directly on the accuracy of the constitutive law that defines the stress-strain relation. Discovering these constitutive material laws remains a significant challenge, in particular when only material deformation data is available. To address this challenge, unsupervised machine learning methods have been proposed. However, existing approaches have several limitations: they either fail to ensure that the learned constitutive relations are consistent with physical principles, or they rely on a predefined library of constitutive relations or manually crafted input features. These dependencies require significant expertise and specialized domain knowledge. Here, we introduce a machine learning approach called uLED, which overcomes the limitations by using the input convex neural network (ICNN) as the surrogate constitutive model. We improve the optimization strategy for training ICNN, allowing it to be trained end-to-end using direct strain invariants as input across various materials. Furthermore, we utilize the nodal force equilibrium at the internal domain as the training objective, which enables us to learn the constitutive relation solely from temporal displacement recordings. We validate the effectiveness of the proposed method on a diverse range of material laws. We demonstrate that it is robust to a significant level of noise and that it converges to the ground truth with increasing data resolution. We also show that the model can be effectively trained using a displacement field from a subdomain of the test specimen and that the learned constitutive relation from one material sample is transferable to other samples with different geometries. The developed methodology provides an effective tool for discovering constitutive relations.

Fault zones mature through the accumulation of earthquakes and the wearing of contact asperities at multiple scales. This study examines how wear-induced gouge production affects the evolution of fault seismicity, focusing on earthquake nucleation, recurrence, and moment partitioning. Using 2D quasi-dynamic simulations integrating rate-and-state friction with Archard's law of wear, we model the space-time distribution of gouge and its effect on the critical slip distance. The study reveals a shift from single to multi-rupture nucleation, marked by increased foreshock activity. The recurrence interval undergoes two separate phases: an initial phase of steady increase followed by a secondary phase of unpredictable behavior. Finally, we observe a transition in the moment partitioning from faster to slower slip rates and a decrease in the moment released per cycle relative to the case where no gouge formation is simulated. This research sheds light on wear-driven mechanisms affecting fault slip behavior, offering valuable insights into how the evolution of gouge along a fault affects its seismic potential.

Nanoparticle surface structural dynamics is believed to play a significant role in regulating functionalities such as diffusion, reactivity, and catalysis but the atomic-level processes are not well understood. Atomic resolution characterization of nanoparticle surface dynamics is challenging since it requires both high spatial and temporal resolution. Though ultrafast transmission electron microscopy (TEM) can achieve picosecond temporal resolution, it is limited to nanometer spatial resolution. On the other hand, with the high readout rate of new electron detectors, conventional TEM has the potential to visualize atomic structure with millisecond time resolutions. However, the need to limit electron dose rates to reduce beam damage yields millisecond images that are dominated by noise, obscuring structural details. Here we show that a newly developed unsupervised denoising framework based on artificial intelligence enables observations of metal nanoparticle surfaces with time resolutions down to 10 ms at moderate electron dose. On this timescale, we find that many nanoparticle surfaces continuously transition between ordered and disordered configurations. The associated stress fields can penetrate below the surface leading to defect formation and destabilization making the entire nanoparticle fluxional. Combining this unsupervised denoiser with electron microscopy greatly improves spatio-temporal characterization capabilities, opening a new window for future exploration of atomic-level structural dynamics in materials.

We introduce DexGanGrasp, a dexterous grasping synthesis method that generates and evaluates grasps with single view in real time. DexGanGrasp comprises a Conditional Generative Adversarial Networks (cGANs)-based DexGenerator to generate dexterous grasps and a discriminator-like DexEvalautor to assess the stability of these grasps. Extensive simulation and real-world expriments showcases the effectiveness of our proposed method, outperforming the baseline FFHNet with an 18.57% higher success rate in real-world evaluation. We further extend DexGanGrasp to DexAfford-Prompt, an open-vocabulary affordance grounding pipeline for dexterous grasping leveraging Multimodal Large Language Models (MLLMs) and Vision Language Models (VLMs), to achieve task-oriented grasping with successful real-world deployments., Comment: 8 pages, 4 figures

Since the development of Brans-Dicke gravity, it has become well-known that a conformal transformation of the metric can reformulate this theory, transferring the coupling of the scalar field from the Ricci scalar to the matter sector. Specifically, in this new frame, known as the Einstein frame, Brans-Dicke gravity is reformulated as General Relativity supplemented by an additional scalar field. In 1959, Hans Adolf Buchdahl utilized an elegant technique to derive a set of solutions for the vacuum field equations within this gravitational framework. In this paper, we extend Buchdahl's method to incorporate the cosmological constant and to the scalar-tensor cases beyond the Brans-Dicke archetypal theory, thereby, with a conformal transformation of the metric, obtaining solutions for a version of Brans-Dicke theory that includes a quadratic potential. More specifically, we obtain synchronous solutions in the following contexts: in scalar-tensor gravity with massless scalar fields, Brans-Dicke theory with a quadratic potential, where we obtain specific synchronous metrics to the Schwarzschild-de Sitter metric, the Nariai solution, and a hyperbolically foliated solution., Comment: 16 pages. Published in Symmetry

Soft lubrication has been shown to drastically affect the mobility of an object immersed in a viscous fluid in the vicinity of a purely elastic wall. In this theoretical study, we develop a minimal model incorporating viscoelasticity, carrying out a perturbation analysis in both the elastic deformation of the wall and its viscous damping. Our approach reveals the singular-perturbation nature of viscoelasticity to soft lubrication. Numerical resolution of the resulting non-linear, singular and coupled equations of motion reveals peculiar effects of viscoelasticity on confined colloidal mobility, opening the way towards the description of complex migration scenarios near realistic polymeric substrates and biological membranes.

Vector AutoRegressive Moving Average (VARMA) models form a powerful and general model class for analyzing dynamics among multiple time series. While VARMA models encompass the Vector AutoRegressive (VAR) models, their popularity in empirical applications is dominated by the latter. Can this phenomenon be explained fully by the simplicity of VAR models? Perhaps many users of VAR models have not fully appreciated what VARMA models can provide. The goal of this review is to provide a comprehensive resource for researchers and practitioners seeking insights into the advantages and capabilities of VARMA models. We start by reviewing the identification challenges inherent to VARMA models thereby encompassing classical and modern identification schemes and we continue along the same lines regarding estimation, specification and diagnosis of VARMA models. We then highlight the practical utility of VARMA models in terms of Granger Causality analysis, forecasting and structural analysis as well as recent advances and extensions of VARMA models to further facilitate their adoption in practice. Finally, we discuss some interesting future research directions where VARMA models can fulfill their potentials in applications as compared to their subclass of VAR models.