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We introduce KANICE (Kolmogorov-Arnold Networks with Interactive Convolutional Elements), a novel neural architecture that combines Convolutional Neural Networks (CNNs) with Kolmogorov-Arnold Network (KAN) principles. KANICE integrates Interactive Convolutional Blocks (ICBs) and KAN linear layers into a CNN framework. This leverages KANs' universal approximation capabilities and ICBs' adaptive feature learning. KANICE captures complex, non-linear data relationships while enabling dynamic, context-dependent feature extraction based on the Kolmogorov-Arnold representation theorem. We evaluated KANICE on four datasets: MNIST, Fashion-MNIST, EMNIST, and SVHN, comparing it against standard CNNs, CNN-KAN hybrids, and ICB variants. KANICE consistently outperformed baseline models, achieving 99.35% accuracy on MNIST and 90.05% on the SVHN dataset. Furthermore, we introduce KANICE-mini, a compact variant designed for efficiency. A comprehensive ablation study demonstrates that KANICE-mini achieves comparable performance to KANICE with significantly fewer parameters. KANICE-mini reached 90.00% accuracy on SVHN with 2,337,828 parameters, compared to KANICE's 25,432,000. This study highlights the potential of KAN-based architectures in balancing performance and computational efficiency in image classification tasks. Our work contributes to research in adaptive neural networks, integrates mathematical theorems into deep learning architectures, and explores the trade-offs between model complexity and performance, advancing computer vision and pattern recognition. The source code for this paper is publicly accessible through our GitHub repository (https://github.com/m-ferdaus/kanice).

Mission-time Linear Temporal Logic (MLTL) is rapidly increasing in popularity as a specification logic, e.g., for runtime verification, model checking, and other formal methods, driving a need for a larger tool base for analysis of this logic. To that end, we formalize formula progression for MLTL in the theorem prover Isabelle/HOL. As groundwork, we first formalize the syntax and semantics for MLTL as well as a verified library of key properties, including useful custom induction rules. We envision this library as being useful for future formalizations involving MLTL and as serving as a reference point for theoretical work using or developing MLTL. We then formalize the algorithm and correctness theorems for formula progression, following the literature. Along the way, we identify and fix several errors and gaps in the source material. A main motivation for our work is tool validation; we ensure the executability of our algorithms by using Isabelle's built-in functionality to generate a code export. This enables both a formal basis for correctly evaluating MLTL formulas and for automatically generating provably correct benchmarks for evaluating tools that reason about MLTL.

We present spectra of 12 of the reddest, and hence dustiest, S stars in the Milky Way, observed with the FORCAST grisms on SOFIA. S stars are asymptotic giant branch (AGB) stars with C/O$\sim$1, so their molecular and dust chemistries are dominated by neither O nor C, often leading to atypical spectral features from their molecules and dust grains. All of the stars in our sample have strong dust emission features at 10--11 $\mu$m, but the shape of the feature in most of the stars differs from the shapes commonly observed in either oxygen-rich or carbon-rich AGB stars. Two stars also show the 13 $\mu$m feature associated with crystalline alumina. Two have a water absorption band at $\sim$6.5--7.5 $\mu$m, and a third has a tentative detection, but only one of these three has the more common SiO absorption band at 7.5 $\mu$m. Three others show a red 6.3 $\mu$m emission feature from complex hydrocarbons consistent with ``Class C'' objects, and in a fourth it appears at 6.37 $\mu$m, redder than even the standard Class C hydrocarbon feature. Class C spectra typically indicate complex hydrocarbons which have been less processed by UV radiation, resulting in more aliphatic bonds relative to aromatic bonds. None of the S stars shows a strong 11.3 $\mu$m hydrocarbon feature, which is also consistent with the presence of aliphatic hydrocarbons.

We describe the spectrophotometric calibration of the Mid-Infrared Instrument's (MIRI) Medium Resolution Spectrometer (MRS) aboard the James Webb Space Telescope (JWST). This calibration is complicated by a time-dependent evolution in the effective throughput of the MRS; this evolution is strongest at long wavelengths, approximately a factor of 2 at 25um over the first two years of the mission. We model and correct for this evolution through regular observations of internal calibration lamps. Pixel flatfields are constructed from observations of the infrared-bright planetary nebula NGC 7027, and photometric aperture corrections from a combination of theoretical models and observations of bright standard stars. We tie the 5--18um flux calibration to high signal/noise (S/N; ~ 600-1000) observations of the O9 V star 10 Lacertae, scaled to the average calibration factor of nine other spectrophotometric standards. We calibrate the 18--28um spectral range using a combination of observations of main belt asteroid 515 Athalia and the circumstellar disk around young stellar object SAO 206462. The photometric repeatability is stable to better than 1% in the wavelength range 5--18um, and the S/N ratio of the delivered spectra is consistent between bootstrapped measurements, pipeline estimates, and theoretical predictions. The MRS point-source calibration agrees with that of the MIRI imager to within 1% from 7 to 21um and is approximately 1% fainter than prior Spitzer observations, while the extended source calibration agrees well with prior Cassini/CIRS and Voyager/IRIS observations., Comment: 29 pages, 32 figures. Submitted to AJ

We present a method for describing nonlinear electromagnetic interactions in integrated photonic devices utilizing an asymptotic-in/out field formalism. Our method expands upon previous continuous wave asymptotic treatments by describing the evolution non-perturbatively for an arbitrary pulsed input. This is presented in the context of a squeezing interaction within an integrated microring resonator side coupled to an input/output waveguide, but is readily generalizable to other integrated structures, while including a variety of (non-squeezing) third-order interactions. An example of a single-pump, non-degenerate squeezing interaction is studied, which is shown to match well with standard coupled-mode treatments for high-finesse resonators, as well as previous perturbative treatments dealing with the generation of pairs with low probability., Comment: 21 pages, 11 figures

The absolute flux calibration of the Mid-Infrared Instrument Imaging and Coronagraphy is based on observations of multiple stars taken during the first 2.5 years of JWST operations. The observations were designed to ensure that the flux calibration is valid for a range of flux densities, different subarrays, and different types of stars. The flux calibration was measured by combining observed aperture photometry corrected to infinite aperture with predictions based on previous observations and models of stellar atmospheres. A subset of these observations were combined with model point-spread-functions to measure the corrections to infinite aperture. Variations in the calibration factor with time, flux density, background level, type of star, subarray, integration time, rate, and well depth were investigated, and the only significant variations were with time and subarray. Observations of the same star taken approximately every month revealed a modest time-dependent response loss seen mainly at the longest wavelengths. This loss is well characterized by a decaying exponential with a time constant of ~200 days. After correcting for the response loss, the band-dependent scatter around the corrected average (aka repeatability) was found to range from 0.1 to 1.2%. Signals in observations taken with different subarrays can be lower by up to 3.4% compared to FULL frame. After correcting for the time and subarray dependencies, the scatter in the calibration factors measured for individual stars ranges from 1 to 4% depending on the band. The formal uncertainties on the flux calibration averaged for all observations are 0.3 to 1.0%, with longer-wavelength bands generally having larger uncertainties., Comment: 26 pages, 17 figures, submitted to AJ

We report JWST MIRI MRS observations of the $\beta$ Pictoris moving group member, $\eta$ Telescopii ($\eta$ Tel) A and its brown dwarf binary companion, $\eta$ Tel B. Following PSF subtraction, we recover the spatially resolved flux from the debris disk around $\eta$ Tel A, along with the position of the companion exterior to the disk. We present a new 5-26 $\mu$m epoch of spectroscopy for the disk, in which we discover a 20 $\mu$m silicate feature. We also present the first ever 11-21 $\mu$m spectrum of $\eta$ Tel B, which indicates a bare photosphere. We derive a new epoch of relative astrometry for the companion, extending the baseline of measurements to 25 years, and find that its current location is consistent with the apocentre of an eccentric, long-period orbit. The companion's orbit is close enough to the disk that it should significantly perturb the planetesimals within it, resulting in a detectable mid-IR pericentre glow and near-alignment with the companion. Contrary to expectations, however, we find that the disk appears to be axisymmetric and potentially misaligned with the companion in the MIRI MRS data. We posit that this may be due to the presence of an additional, yet-undetected 0.7-30 $M_J$ planet orbiting interior to the disk with a semi-major axis of 3-19 au., Comment: 27 pages, 19 figures, accepted for publication in ApJ

Imbalanced datasets are a significant challenge in real-world scenarios. They lead to models that underperform on underrepresented classes, which is a critical issue in infrastructure inspection. This paper introduces the Enhanced Feature Pyramid Network (E-FPN), a deep learning model for the semantic segmentation of culverts and sewer pipes within imbalanced datasets. The E-FPN incorporates architectural innovations like sparsely connected blocks and depth-wise separable convolutions to improve feature extraction and handle object variations. To address dataset imbalance, the model employs strategies like class decomposition and data augmentation. Experimental results on the culvert-sewer defects dataset and a benchmark aerial semantic segmentation drone dataset show that the E-FPN outperforms state-of-the-art methods, achieving an average Intersection over Union (IoU) improvement of 13.8% and 27.2%, respectively. Additionally, class decomposition and data augmentation together boost the model's performance by approximately 6.9% IoU. The proposed E-FPN presents a promising solution for enhancing object segmentation in challenging, multi-class real-world datasets, with potential applications extending beyond culvert-sewer defect detection.

This paper introduces Semantic Haar-Adaptive Refined Pyramid Network (SHARP-Net), a novel architecture for semantic segmentation. SHARP-Net integrates a bottom-up pathway featuring Inception-like blocks with varying filter sizes (3x3$ and 5x5), parallel max-pooling, and additional spatial detection layers. This design captures multi-scale features and fine structural details. Throughout the network, depth-wise separable convolutions are used to reduce complexity. The top-down pathway of SHARP-Net focuses on generating high-resolution features through upsampling and information fusion using $1\times1$ and $3\times3$ depth-wise separable convolutions. We evaluated our model using our developed challenging Culvert-Sewer Defects dataset and the benchmark DeepGlobe Land Cover dataset. Our experimental evaluation demonstrated the base model's (excluding Haar-like features) effectiveness in handling irregular defect shapes, occlusions, and class imbalances. It outperformed state-of-the-art methods, including U-Net, CBAM U-Net, ASCU-Net, FPN, and SegFormer, achieving average improvements of 14.4% and 12.1% on the Culvert-Sewer Defects and DeepGlobe Land Cover datasets, respectively, with IoU scores of 77.2% and 70.6%. Additionally, the training time was reduced. Furthermore, the integration of carefully selected and fine-tuned Haar-like features enhanced the performance of deep learning models by at least 20%. The proposed SHARP-Net, incorporating Haar-like features, achieved an impressive IoU of 94.75%, representing a 22.74% improvement over the base model. These features were also applied to other deep learning models, showing a 35.0% improvement, proving their versatility and effectiveness. SHARP-Net thus provides a powerful and efficient solution for accurate semantic segmentation in challenging real-world scenarios.

In previous work I have shown that Herglotz actions reproduce the dynamics of classical mechanical theories which exhibit dynamical similarities. Recent work has shown how to extend field theories in both the Lagrangian and de Donder-Weyl formalism to contact geometry. In this article I show how dynamical similarity applies in field theory. This is applied in both the Lagrangian and Hamiltonian frameworks, producing the contact equivalents. The result can be applied to general relativity where I demonstrate how to construct a complete description of the dynamics, equivalent to those derived from the Einstein-Hilbert action, without reference to the conformal factor.

Modeling observations of the archetypal debris disk around $\beta$ Pic, obtained in 2023 January with the MIRI MRS on board JWST, reveals significant differences compared with that obtained with the IRS on board Spitzer. The bright 5 - 15 $\mu$m continuum excess modeled using a $\sim$600 K black body has disappeared. The previously prominent 18 and 23 $\mu$m crystalline forsterite emission features, arising from cold dust ($\sim$100 K) in the Rayleigh limit, have disappeared and been replaced by very weak features arising from the hotter 500 K dust population. Finally, the shape of the 10 $\mu$m silicate feature has changed, consistent with a shift in the temperature of the warm dust population from $\sim$300 K to $\sim$500 K and an increase in the crystalline fraction of the warm, silicate dust. Stellar radiation pressure may have blown both the hot and the cold crystalline dust particles observed in the Spitzer spectra out of the planetary system during the intervening 20 years between the Spitzer and JWST observations. These results indicate that the $\beta$ Pic system has a dynamic circumstellar environment, and that periods of enhanced collisions can create large clouds of dust that sweep through the planetary system., Comment: 15 pages, 8 figures, ApJ in press

The JWST has been collecting scientific data for over two years now. Scientists are now looking deeper into the data, which introduces the need to correct known systematic effects. Important limiting factors for the MIRI/MRS are the pointing accuracy, non-linearity, detector charge migration, detector scattering, the accuracy of the PSF model, and the complex interplay between these. The Cycle 2 programme 3779 proposed a 72-point intra-pixel dither raster of the calibration star 10-Lac. In this first work of the paper series, we aim to address the degeneracy between the non-linearity and BFE that affect the pixel voltage integration ramps of the MRS. Due to the low flux in the longer wavelengths, we only do this in the 4.9 to 11.7 micron region. We fitted the ramps per pixel and dither, in order to fold in the deviations from classical non-linearity that are caused by charge migration. The ramp shapes should be repeatable depending on the part of the PSF that is sampled. By doing so, we defined both a grid-based linearity correction, and an interpolated linearity correction. We find significant improvements compared to the uniform illumination assumption. The standard deviation on the pixel ramp residual non-linearity is between 70-90% smaller than the current standard pipeline when self-calibrating with the grid. We are able to interpolate these coefficients to apply to any unresolved source not on the grid points, resulting in an up to 70% smaller standard deviation on the residual deviation from linearity. The FWHM is up to 20% narrower. The depth of the fringes is now consistent up the ramp. Pointing-specific linearity corrections allow us to fix the systematic deviation in the slopes. We demonstrated this for unresolved sources. The discovered trends with PSF sampling suggest that, we may be able to model ramps for spatially extended and resolved illumination as well., Comment: 18 pages, 20 figures, Accepted for publication in A&A

Graph theory has been a powerful tool in solving difficult and complex problems arising in all disciplines. In particular, graph matching is a classical problem in pattern analysis with enormous applications. Many graph problems have been formulated as a mathematical program and then solved using exact, heuristic, and/or approximated-guaranteed procedures. On the other hand, graph theory has been a powerful tool in visualizing and understanding complex mathematical programming problems, especially integer programs. Formulating a graph problem as a natural integer program (IP) is often a challenging task. However, an IP formulation of the problem has many advantages. Several researchers have noted the need for natural IP formulation of graph theoretic problems. The present study aims to provide a unified framework for IP formulation of graph-matching problems. Although there are many surveys on graph matching problems, none is concerned with IP formulation. This paper is the first to provide a comprehensive IP formulation for such problems. The framework includes a variety of graph optimization problems in the literature. While these problems have been studied by different research communities, however, the framework presented here helps to bring efforts from different disciplines to tackle such diverse and complex problems. We hope the present study can significantly help to simplify some of the difficult problems arising in practice, especially in pattern analysis., Comment: 34 pages

We consider learning in an adversarial environment, where an $\varepsilon$-fraction of samples from a distribution $P$ are arbitrarily modified (global corruptions) and the remaining perturbations have average magnitude bounded by $\rho$ (local corruptions). Given access to $n$ such corrupted samples, we seek a computationally efficient estimator $\hat{P}_n$ that minimizes the Wasserstein distance $\mathsf{W}_1(\hat{P}_n,P)$. In fact, we attack the fine-grained task of minimizing $\mathsf{W}_1(\Pi_\# \hat{P}_n, \Pi_\# P)$ for all orthogonal projections $\Pi \in \mathbb{R}^{d \times d}$, with performance scaling with $\mathrm{rank}(\Pi) = k$. This allows us to account simultaneously for mean estimation ($k=1$), distribution estimation ($k=d$), as well as the settings interpolating between these two extremes. We characterize the optimal population-limit risk for this task and then develop an efficient finite-sample algorithm with error bounded by $\sqrt{\varepsilon k} + \rho + \tilde{O}(d\sqrt{k}n^{-1/(k \lor 2)})$ when $P$ has bounded covariance. This guarantee holds uniformly in $k$ and is minimax optimal up to the sub-optimality of the plug-in estimator when $\rho = \varepsilon = 0$. Our efficient procedure relies on a novel trace norm approximation of an ideal yet intractable 2-Wasserstein projection estimator. We apply this algorithm to robust stochastic optimization, and, in the process, uncover a new method for overcoming the curse of dimensionality in Wasserstein distributionally robust optimization., Comment: Accepted for presentation at the Conference on Learning Theory (COLT) 2024

Optical computing often employs tailor-made hardware to implement specific algorithms, trading generality for improved performance in key aspects like speed and power efficiency. An important computing approach that is still missing its corresponding optical hardware is probabilistic computing, used e.g. for solving difficult combinatorial optimization problems. In this study, we propose an experimentally viable photonic approach to solve arbitrary probabilistic computing problems. Our method relies on the insight that coherent Ising machines composed of coupled and biased optical parametric oscillators can emulate stochastic logic. We demonstrate the feasibility of our approach by using numerical simulations equivalent to the full density matrix formulation of coupled optical parametric oscillators.

The rapid progress in Large Language Models (LLMs) could transform many fields, but their fast development creates significant challenges for oversight, ethical creation, and building user trust. This comprehensive review looks at key trust issues in LLMs, such as unintended harms, lack of transparency, vulnerability to attacks, alignment with human values, and environmental impact. Many obstacles can undermine user trust, including societal biases, opaque decision-making, potential for misuse, and the challenges of rapidly evolving technology. Addressing these trust gaps is critical as LLMs become more common in sensitive areas like finance, healthcare, education, and policy. To tackle these issues, we suggest combining ethical oversight, industry accountability, regulation, and public involvement. AI development norms should be reshaped, incentives aligned, and ethics integrated throughout the machine learning process, which requires close collaboration across technology, ethics, law, policy, and other fields. Our review contributes a robust framework to assess trust in LLMs and analyzes the complex trust dynamics in depth. We provide contextualized guidelines and standards for responsibly developing and deploying these powerful AI systems. This review identifies key limitations and challenges in creating trustworthy AI. By addressing these issues, we aim to build a transparent, accountable AI ecosystem that benefits society while minimizing risks. Our findings provide valuable guidance for researchers, policymakers, and industry leaders striving to establish trust in LLMs and ensure they are used responsibly across various applications for the good of society., Comment: Under review at Proceedings of the IEEE

In this paper we present a framework in which the relational description of General Relativity can be used to smoothly continue cosmological dynamical systems through the Big Bang without invoking quantum gravity effects. Cosmological spacetimes contain as a key dynamical variable a notion of scale through the volume factor $\nu$. However no cosmological observer is ever able to separate their measuring apparatus from the system they are measuring, in that sense every measurement is a relative one and measurable dynamical variables are in fact dimensionless ratios. This is manifest in the identification of a scaling symmetry or ``Dynamical Similarity" in the Einstein-Hilbert action associated with the volume factor. By quotienting out this scaling symmetry, we form a relational system defined on a contact manifold whose dynamical variables are decoupled from scale. When the phase space is reduced to shape space, we show that there exist unique solutions to the equations of motion that pass smoothly through the initial cosmological singularity in flat FLRW, Bianchi I and Quiescent Bianchi IX cosmologies., Comment: 52 pages, 38 figures

Increasing demands on medical imaging departments are taking a toll on the radiologist's ability to deliver timely and accurate reports. Recent technological advances in artificial intelligence have demonstrated great potential for automatic radiology report generation (ARRG), sparking an explosion of research. This survey paper conducts a methodological review of contemporary ARRG approaches by way of (i) assessing datasets based on characteristics, such as availability, size, and adoption rate, (ii) examining deep learning training methods, such as contrastive learning and reinforcement learning, (iii) exploring state-of-the-art model architectures, including variations of CNN and transformer models, (iv) outlining techniques integrating clinical knowledge through multimodal inputs and knowledge graphs, and (v) scrutinising current model evaluation techniques, including commonly applied NLP metrics and qualitative clinical reviews. Furthermore, the quantitative results of the reviewed models are analysed, where the top performing models are examined to seek further insights. Finally, potential new directions are highlighted, with the adoption of additional datasets from other radiological modalities and improved evaluation methods predicted as important areas of future development., Comment: 24 pages, 8 figures, 6 tables. Accepted by IEEE Reviews in Biomedical Engineering

Lasair is the UK Community Broker for transient alerts from the Legacy Survey of Space and Time (LSST) from the Vera C. Rubin Observatory. We explain the system's capabilities, how users can achieve their scientific goals, and how Lasair is implemented. Lasair offers users a kit of parts that they can use to build filters to concentrate their desired alerts. The kit has novel lightcurve features, sky context, watchlists of special sky objects and regions of the sky, dynamic crossmatching with catalogues of known astronomical sources, and classifications and annotations from other users and partner projects. These resources can be shared with other users, copied, and modified. Lasair offers real-time machine-to-machine notifications of filtered transient alerts. Even though the Rubin Observatory is not yet complete, Lasair is a mature system: it has been processing and serving data from the similarly formatted stream of the Zwicky Transient Facility (ZTF) alerts.

The Mid-Infrared Instrument (MIRI) aboard the James Webb Space Telescope (JWST) provides the observatory with a huge advance in mid-infrared imaging and spectroscopy covering the wavelength range of 5 to 28 microns. This paper describes the performance and characteristics of the MIRI imager as understood during observatory commissioning activities, and through its first year of science operations. We discuss the measurements and results of the imager's point spread function, flux calibration, background, distortion and flat fields as well as results pertaining to best observing practices for MIRI imaging, and discuss known imaging artefacts that may be seen during or after data processing. Overall, we show that the MIRI imager has met or exceeded all its pre-flight requirements, and we expect it to make a significant contribution to mid-infrared science for the astronomy community for years to come.

Classical cosmology exhibits a particular kind of scaling symmetry. The dynamics of the invariants of this symmetry forms a system that exhibits many of the features of open systems such as the non-conservation of mechanical energy and the focusing of measures along the dynamical flow. From these properties, we show that important dynamical features emerge that are not present in closed systems. In particular, a large and physically plausible class of cosmological models give rise to a natural arrow of time. We then argue that the appropriate notion of closure in cosmology is dynamical closure - that a system can be integrated without reference to external factors. This is realised in physical systems in terms of the algebraic closure of the equations of motion such that the system is autonomous. Remarkably, in a growing class of models it can be shown that the autonomous system obtained remains regular and can be integrated through the big bang., Comment: 11 pages

We present point-source photometry from the Spitzer Space Telescope's final survey of the Small Magellanic Cloud (SMC). We mapped 30 square degrees in two epochs in 2017, with the second extending to early 2018 at 3.6 and 4.5 microns using the Infrared Array Camera. This survey duplicates the footprint from the SAGE-SMC program in 2008. Together, these surveys cover a nearly 10 yr temporal baseline in the SMC. We performed aperture photometry on the mosaicked maps produced from the new data. We did not use any prior catalogs as inputs for the extractor in order to be sensitive to any moving objects (e.g., foreground brown dwarfs) and other transient phenomena (e.g., cataclysmic variables or FU Ori-type eruptions). We produced a point-source catalog with high-confidence sources for each epoch as well as combined-epoch catalog. For each epoch and the combined-epoch data, we also produced a more complete archive with lower-confidence sources. All of these data products will be available to the community at the Infrared Science Archive., Comment: 16 pages, 11 figures, 6 tables

Probabilistic machine learning utilizes controllable sources of randomness to encode uncertainty and enable statistical modeling. Harnessing the pure randomness of quantum vacuum noise, which stems from fluctuating electromagnetic fields, has shown promise for high speed and energy-efficient stochastic photonic elements. Nevertheless, photonic computing hardware which can control these stochastic elements to program probabilistic machine learning algorithms has been limited. Here, we implement a photonic probabilistic computer consisting of a controllable stochastic photonic element - a photonic probabilistic neuron (PPN). Our PPN is implemented in a bistable optical parametric oscillator (OPO) with vacuum-level injected bias fields. We then program a measurement-and-feedback loop for time-multiplexed PPNs with electronic processors (FPGA or GPU) to solve certain probabilistic machine learning tasks. We showcase probabilistic inference and image generation of MNIST-handwritten digits, which are representative examples of discriminative and generative models. In both implementations, quantum vacuum noise is used as a random seed to encode classification uncertainty or probabilistic generation of samples. In addition, we propose a path towards an all-optical probabilistic computing platform, with an estimated sampling rate of ~ 1 Gbps and energy consumption of ~ 5 fJ/MAC. Our work paves the way for scalable, ultrafast, and energy-efficient probabilistic machine learning hardware.

A search has been carried out for Magnetized Quark Nuggets (MQNs) accumulating in iron ore over geologic time. MQNs, which are theoretically consistent with the Standard Models of Physics and of Cosmology, have been suggested as dark-matter candidates. Indirect evidence of MQNs has been previously inferred from observations of magnetars and of non-meteorite impact craters. It is shown in this paper that MQNs can accumulate in taconite (iron ore) and be transferred into ferromagnetic rod-mill liners during processing of the ore. When the liners are recycled to make fresh steel, they are heated to higher than the Curie temperature so that their ferromagnetic properties are destroyed. The MQNs would then be released and fall into the ferromagnetic furnace bottom where they would be trapped. Three such furnace bottoms have been magnetically scanned to search for the magnetic anomalies consistent with trapped MQNs. The observed magnetic anomalies are equivalent to an accumulation rate of ~1 kg of MQNs per 1.2 x $10^8$ kg of taconite ore processed. The results are consistent with MQNs but there could be other, unknown explanations. We propose an experiment and calculations to definitively test the MQN hypothesis for dark matter., Comment: 30 pages, 11 figures, 5 tables

We present JWST MIRI Medium Resolution Spectrograph (MRS) observations of the $\beta$ Pictoris system. We detect an infrared excess from the central unresolved point source from 5 to 7.5 $\mu$m which is indicative of dust within the inner $\sim$7 au of the system. We perform PSF subtraction on the MRS data cubes and detect a spatially resolved dust population emitting at 5 $\mu$m. This spatially resolved hot dust population is best explained if the dust grains are in the small grain limit (2$\pi$a$\ll$$\lambda$). The combination of unresolved and resolved dust at 5 $\mu$m could suggest that dust grains are being produced in the inner few au of the system and are then radiatively driven outwards, where the particles could accrete onto the known planets in the system $\beta$ Pic b and c. We also report the detection of an emission line at 6.986 $\mu$m that we attribute to be [Ar II]. We find that the [Ar II] emission is spatially resolved with JWST and appears to be aligned with the dust disk. Through PSF subtraction techniques, we detect $\beta$ Pic b at the 5$\sigma$ level in our MRS data cubes and present the first mid-IR spectrum of the planet from 5 to 7 $\mu$m. The planet's spectrum is consistent with having absorption from water vapor between 5 and 6.5 $\mu$m. We perform atmosphere model grid fitting on spectra and photometry of $\beta$ Pic b and find that the planet's atmosphere likely has a sub-stellar C/O ratio., Comment: Accepted for Publication in ApJ

Advances in natural language processing and understanding have led to a rapid growth in the popularity of conversational user interfaces (CUIs). While CUIs introduce novel benefits, they also yield risks that may exploit people's trust. Although research looking at unethical design deployed through graphical user interfaces (GUIs) established a thorough understanding of so-called dark patterns, there is a need to continue this discourse within the CUI community to understand potentially problematic interactions. Addressing this gap, we interviewed 27 participants from three cohorts: researchers, practitioners, and frequent users of CUIs. Applying thematic analysis, we construct five themes reflecting each cohort's insights about ethical design challenges and introduce the CUI Expectation Cycle, bridging system capabilities and user expectations while considering each theme's ethical caveats. This research aims to inform future development of CUIs to consider ethical constraints while adopting a human-centred approach., Comment: 18 pages; 4 tables; and 1 figure. This is the author's version and pre-print of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record will be published in Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI '24), May 11--16, 2024, Honolulu, HI, USA, https://doi.org/https://doi.org/10.1145/3613904.3642542

Quantum states of light, particularly at optical frequencies, are considered necessary to realize a host of important quantum technologies and applications, spanning Heisenberg-limited metrology, continuous-variable quantum computing, and quantum communications. Nevertheless, a wide variety of important quantum light states are currently challenging to deterministically generate at optical frequencies. In part, this is due to a relatively small number of schemes that prepare target quantum states given nonlinear interactions. Here, we present an especially simple concept for deterministically generating and stabilizing important quantum states of light, using only simple third-order optical nonlinearities and engineered dissipation. We show how by considering either a nonlinear cavity with frequency-dependent outcoupling, or a chain of nonlinear waveguides, one can "filter" out all but a periodic ladder of photon number components of a density matrix. As examples of this phenomenon, we show cavities which can stabilize squeezed states, as well as produce "photon-number-comb" states. Moreover, in these types of filter cavities, Glauber coherent states will deterministically evolve into Schrodinger cat states of a desired order. We discuss potential realizations in quantum nonlinear optics. More broadly, we expect that combining the techniques introduced here with additional "phase-sensitive" nonlinearities (such as second-order nonlinearity) should enable passive stabilization and generation of a wider variety of states than shown here.