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A correlational study was conducted using ACT (American College Test) sub-test scores and actual grades earned in corresponding subject areas in order to determine if the ACT was an effective predictor for student's grades at Southern West Virginia Community College (SWVCC). Study subjects were all students (n= 241) who had ACT test scores on file and who were enrolled at SWVCC. Correlational analyses yielded coefficients of such low magnitude as to conclude that there is no significant relationship between ACT test scores and academic performance at SWVCC. Based on these findings, it was recommended that use of the ACT be discontinued and that a more suitable instrument such as the College Qualifying Test or the Comparative Guidance and Placement Test, both of which have been developed for and normed on community college students, be used. (Author/JDS)

Deep learning-based methods that jointly perform the task of acoustic echo and noise reduction (AENR) often require high memory and computational resources, making them unsuitable for real-time deployment on low-resource platforms such as embedded devices. We propose a low-complexity hybrid approach for joint AENR by employing a single model to suppress both residual echo and noise components. Specifically, we integrate the state-of-the-art (SOTA) ULCNet model, which was originally proposed to achieve ultra-low complexity noise suppression, in a hybrid system and train it for joint AENR. We show that the proposed approach achieves better echo reduction and comparable noise reduction performance with much lower computational complexity and memory requirements than all considered SOTA methods, at the cost of slight degradation in speech quality., Comment: 5 pages, 2 figures

A primary science goal for JWST is to detect and characterize the atmospheres of terrestrial planets orbiting M dwarfs (M-Earths). The existence of atmospheres on M-Earths is highly uncertain because their host stars' extended history of high XUV irradiation may act to completely remove their atmospheres. We present two JWST secondary eclipse observations of the M-Earth Gl 486b (also known as GJ 486b) between 5-12 $\mu$m. We combined these observations with a precise analysis of the host star parameters to derive a planetary dayside temperature of $T_{p}=865 \pm 14$ K. We compared this temperature to the maximum expected temperature for a zero albedo, zero heat redistribution bare rock and derived a temperature ratio of $R=\frac{T_{p,dayside}}{T_{p,max}}=0.97 \pm 0.01$. This value is consistent with an airless body with a slight non-zero albedo or a thin atmosphere with $<1$% H$_{2}$O or $<1$ ppm CO$_{2}$. However, it is inconsistent with an Earth- or Venus-like atmosphere, and the spectrum shows no clear emission or absorption features. Additionally, our observations are inconsistent with the water-rich atmospheric scenario allowed by previous transit observations and suggest the transmission spectrum was instead shaped by stellar contamination (Moran et al. 2023). Given the potential for atmospheric escape throughout the system's $\geq6.6$-Gyr lifetime (Diamond-Lowe et al. 2024), we conclude that the observations are likely best explained by an airless planet. This result is the most precise measurement yet of terrestrial exoplanet thermal emission with JWST, which places a strong constraint on the position of the "Cosmic Shoreline" between airless bodies and those with atmospheres., Comment: 13 pages, 6 figures, submitted to ApJL

In 2017, the next generation Very Large Array (ngVLA) Science Advisory Council, together with the international astronomy community, developed a set of five Key Science Goals (KSGs) to inform, prioritize and refine the technical capabilities of a future radio telescope array for high angular resolution operation from 1.2 - 116 GHz with 10 times the sensitivity of the Jansky VLA and ALMA. The resulting KSGs, which require observations at centimeter and millimeter wavelengths that cannot be achieved by any other facility, represent a small subset of the broad range of astrophysical problems that the ngVLA will be able address. This document presents an update to the original ngVLA KSGs, taking account of new results and progress in the 7+ years since their initial presentation, again drawing on the expertise of the ngVLA Science Advisory Council and the broader community in the ngVLA Science Working Groups. As the design of the ngVLA has also matured substantially in this period, this document also briefly addresses initial expectations for ngVLA data products and processing that will be needed to achieve the KSGs. The original ngVLA KSGs endure as outstanding problems of high priority. In brief, they are: (1) Unveiling the Formation of Solar System Analogues; (2) Probing the Initial Conditions for Planetary Systems and Life with Astrochemistry; (3) Charting the Assembly, Structure, and Evolution of Galaxies from the First Billion Years to the Present; (4) Science at the Extremes: Pulsars as Laboratories for Fundamental Physics; (5) Understanding the Formation and Evolution of Stellar and Supermassive Black Holes in the Era of Multi-Messenger Astronomy., Comment: 29 pages, 9 figures, ngVLA memo 125. arXiv admin note: text overlap with arXiv:1711.09960

We present thermal emission measurements of GJ 1132b spanning 5--12 um obtained with the Mid-Infrared Instrument Low-Resolution Spectrometer (MIRI/LRS) on the James Webb Space Telescope (JWST). GJ 1132b is an M-dwarf rocky planet with Teq=584 K and an orbital period of 1.6 days. We measure a white-light secondary eclipse depth of 140+/-17 ppm, which corresponds to a dayside brightness temperature of Tp,dayside= 709+/-31 K using improved star and planet parameters. This measured temperature is only 1 sigma below the maximum possible dayside temperature of a bare rock (i.e., assuming a zero albedo planet with no heat redistribution, Tmax = 746+14/-11 K). The emission spectrum is consistent with a featureless blackbody, which agrees with a wide range of possible surface compositions. By comparing forward models to the dayside emission spectrum, we rule out Earth-thickness (P ~ 1 bar) atmospheres with at least 1% H2O, atmospheres of any modeled thickness (10^-4 -- 10^2 bar) that contain at least 1% CO2, and thick, Venus-like atmospheres (P>~100 bar) with at least 1 ppm CO2 or H2O. We therefore conclude that GJ 1132b likely does not have a significant atmosphere. This finding supports the concept of a universal 'Cosmic Shoreline' given the high level of bolometric and XUV irradiation received by the planet., Comment: Accepted by ApJL

In low-energy collisions between two dipolar molecules, the long-range dipole-dipole interaction plays an important role in the scattering dynamics. Merged beam configurations offer the lowest collision energies achievable, but they generally can not be applied to most dipole-dipole systems as the electrodes used to merge one beam would deflect the other. This paper covers the design and implementation of a merged electrostatic guide whose geometry was numerically optimized for ND$_3$-ND$_3$ and ND$_3$-NH$_3$ collisions. This device guides both beams simultaneously and makes them converge up to an effective collision angle of 2$\mathrm{^\circ}$, yielding the optimal compromise between spatial overlap and the lowest possible collision energy. We present preliminary data for inelastic ND$_3$-ND$_3$ collisions.

Soft-tissue and bone tumours (STBT) are rare, diagnostically challenging lesions with variable clinical behaviours and treatment approaches. This systematic review provides an overview of Artificial Intelligence (AI) methods using radiological imaging for diagnosis and prognosis of these tumours, highlighting challenges in clinical translation, and evaluating study alignment with the Checklist for AI in Medical Imaging (CLAIM) and the FUTURE-AI international consensus guidelines for trustworthy and deployable AI to promote the clinical translation of AI methods. The review covered literature from several bibliographic databases, including papers published before 17/07/2024. Original research in peer-reviewed journals focused on radiology-based AI for diagnosing or prognosing primary STBT was included. Exclusion criteria were animal, cadaveric, or laboratory studies, and non-English papers. Abstracts were screened by two of three independent reviewers for eligibility. Eligible papers were assessed against guidelines by one of three independent reviewers. The search identified 15,015 abstracts, from which 325 articles were included for evaluation. Most studies performed moderately on CLAIM, averaging a score of 28.9$\pm$7.5 out of 53, but poorly on FUTURE-AI, averaging 5.1$\pm$2.1 out of 30. Imaging-AI tools for STBT remain at the proof-of-concept stage, indicating significant room for improvement. Future efforts by AI developers should focus on design (e.g. define unmet clinical need, intended clinical setting and how AI would be integrated in clinical workflow), development (e.g. build on previous work, explainability), evaluation (e.g. evaluating and addressing biases, evaluating AI against best practices), and data reproducibility and availability (making documented code and data publicly available). Following these recommendations could improve clinical translation of AI methods., Comment: 23 pages, 6 figures, 6 supplementary figures

Quantum computing has tremendous potential to overcome some of the fundamental limitations present in classical information processing. Yet, today's technological limitations in the quality and scaling prevent exploiting its full potential. Quantum computing based on superconducting quantum processing units (QPUs) is among the most promising approaches towards practical quantum advantage. In this article the basic technological approach of IQM Quantum Computers is described covering both the QPU and the rest of the full-stack quantum computer. In particular, the focus is on a 20-qubit quantum computer featuring the Garnet QPU and its architecture, which we will scale up to 150 qubits. We also present QPU and system-level benchmarks, including a median 2-qubit gate fidelity of 99.5% and genuinely entangling all 20 qubits in a Greenberger-Horne-Zeilinger (GHZ) state.

Magnetic adatoms on s-wave superconductors induce bound states inside the superconducting gap, called Yu-Shiba-Rusinov states (YSR). The anisotropy of the Fermi surface determines the spatial extension of bound states in a quasi-two-dimensional superconductor. This is especially important in the diluted impurity limit since the orbital overlap determines the coupling of YSR states of neighboring atoms and the formation of the collective YSR system. Here, we build diluted arrays of Mn atoms with different dimensionalities on the surface of $\beta$-Bi$_2$Pd, and we measure the evolution of their YSR spectra with the structure. We detect the coupling as a split of YSR peaks in subgap spectra and find that the split size increases with the number of atoms. The orientation of the structures along different directions of the \bipd substrate modulates the split and particle-hole asymmetry of the YSR states due to the anisotropic character of the Fermi surface, captured by the Green function model. With the aid of the model, we found multiple YSR excitations in an extended 2D array of 25 Mn atoms, and we identified that their spatial distribution reflects a chiral LDOS., Comment: 10 pages, 5 figures

Detecting out-of-distribution (OOD) data is crucial in machine learning applications to mitigate the risk of model overconfidence, thereby enhancing the reliability and safety of deployed systems. The majority of existing OOD detection methods predominantly address uni-modal inputs, such as images or texts. In the context of multi-modal documents, there is a notable lack of extensive research on the performance of these methods, which have primarily been developed with a focus on computer vision tasks. We propose a novel methodology termed as attention head masking (AHM) for multi-modal OOD tasks in document classification systems. Our empirical results demonstrate that the proposed AHM method outperforms all state-of-the-art approaches and significantly decreases the false positive rate (FPR) compared to existing solutions up to 7.5\%. This methodology generalizes well to multi-modal data, such as documents, where visual and textual information are modeled under the same Transformer architecture. To address the scarcity of high-quality publicly available document datasets and encourage further research on OOD detection for documents, we introduce FinanceDocs, a new document AI dataset. Our code and dataset are publicly available.

In cavity quantum electrodynamics (QED), emitters and a resonator are coupled together to enable precise studies of quantum light-matter interactions. Over the past few decades, this has led to a variety of quantum technologies such as more precise inertial sensors, clocks, memories, controllable qubits, and quantum simulators. Furthermore, the intrinsically dissipative nature of cavity QED platforms makes them a natural testbed for exploring driven-dissipative phenomena in open quantum systems as well as equilibrium and non-equilibrium phase transitions in quantum optics. One such model, the so-called cooperative resonance fluorescence (CRF) model, concerns the behavior of coherently driven emitters in the presence of collective dissipation (superradiance). Despite tremendous interest, this model has yet to be realized in a clean experimental system. Here we provide an observation of the continuous superradiant phase transition predicted in the CRF model using an ensemble of ultracold $^{88}$Sr atoms coupled to a driven high-finesse optical cavity on a long-lived optical transition. Below a critical drive, atoms quickly reach a steady state determined by the self-balancing of the drive and the collective dissipation. The steady state possesses a macroscopic dipole moment and corresponds to a superradiant phase. Above a critical drive strength, the atoms undergo persistent Rabi-like oscillations until other decoherence processes kick in. In fact, our platform also allows us to witness the change of this phase transition from second to first order induced by single-particle spontaneous emission, which pushes the system towards a different steady state. Our observations are a first step towards finer control of driven-dissipative systems, which have been predicted to generate quantum states that can be harnessed for quantum information processing and in particular quantum sensing., Comment: 12 pages, 7 figures (main text and methods); 11 pages, 4 figures (supplementary information); typos corrected

We analyse the impact of using tempered likelihoods in the production of posterior predictions. Our findings reveal that once the sample size is at least moderately large and the temperature is not too small, then likelihood tempering has virtually no impact on the resulting posterior predictions., Comment: 7 (+14 supplementary) pages (excluding references); 3 (+4 supplementary) figures

We introduce Josephson junction and inductor models in Keysight ADS that feature an auxiliary flux port, and facilitate the expression of flux quantization conditions in simulation of superconducting microwave circuits. We present several examples that illustrate our methodology for constructing flux-quantizing circuits, including dc- and rf-SQUIDs, tunable couplers, and parametric amplifiers using SNAIL and rf-SQUID arrays. We perform DC, S-parameter, and harmonic balance simulations to validate our models and methods against theory and published experimental results., Comment: 11 pages, 14 figures

Recent surge in Large Language Model (LLM) availability has opened exciting avenues for research. However, efficiently interacting with these models presents a significant hurdle since LLMs often reside on proprietary or self-hosted API endpoints, each requiring custom code for interaction. Conducting comparative studies between different models can therefore be time-consuming and necessitate significant engineering effort, hindering research efficiency and reproducibility. To address these challenges, we present prompto, an open source Python library which facilitates asynchronous querying of LLM endpoints enabling researchers to interact with multiple LLMs concurrently, while maximising efficiency and utilising individual rate limits. Our library empowers researchers and developers to interact with LLMs more effectively and enabling faster experimentation and evaluation. prompto is released with an introductory video (https://youtu.be/-eZAmlV4ypk) under MIT License and is available via GitHub (https://github.com/alan-turing-institute/prompto).

We discuss elliptic quantum Calogero-Moser-Sutherland models, including their relativistic generalizations due to Ruijsenaars and van Diejen, and the relations of these models to classes of special functions developed and explored in recent and on-going research efforts. We give an introduction to these models and corresponding special functions aimed at non-experts. We also describe a few open problems in the field., Comment: 31 pages. Invited contribution to the 2nd edition of the Encyclopedia of Mathematical Physics

A central pillar of the UK's response to the SARS-CoV-2 pandemic was the provision of up-to-the moment nowcasts and short term projections to monitor current trends in transmission and associated healthcare burden. Here we present a detailed deconstruction of one of the 'real-time' models that was key contributor to this response, focussing on the model adaptations required over three pandemic years characterised by the imposition of lockdowns, mass vaccination campaigns and the emergence of new pandemic strains. The Bayesian model integrates an array of surveillance and other data sources including a novel approach to incorporating prevalence estimates from an unprecedented large-scale household survey. We present a full range of estimates of the epidemic history and the changing severity of the infection, quantify the impact of the vaccination programme and deconstruct contributing factors to the reproduction number. We further investigate the sensitivity of model-derived insights to the availability and timeliness of prevalence data, identifying its importance to the production of robust estimates., Comment: 35 pages, 7 figures, 3 tables

The rapid integration of machine learning methodologies in healthcare has ignited innovative strategies for disease prediction, particularly with the vast repositories of Electronic Health Records (EHR) data. This article delves into the realm of multi-disease prediction, presenting a comprehensive study that introduces a pioneering ensemble feature selection model. This model, designed to optimize learning systems, combines statistical, deep, and optimally selected features through the innovative Stabilized Energy Valley Optimization with Enhanced Bounds (SEV-EB) algorithm. The objective is to achieve unparalleled accuracy and stability in predicting various disorders. This work proposes an advanced ensemble model that synergistically integrates statistical, deep, and optimally selected features. This combination aims to enhance the predictive power of the model by capturing diverse aspects of the health data. At the heart of the proposed model lies the SEV-EB algorithm, a novel approach to optimal feature selection. The algorithm introduces enhanced bounds and stabilization techniques, contributing to the robustness and accuracy of the overall prediction model. To further elevate the predictive capabilities, an HSC-AttentionNet is introduced. This network architecture combines deep temporal convolution capabilities with LSTM, allowing the model to capture both short-term patterns and long-term dependencies in health data. Rigorous evaluations showcase the remarkable performance of the proposed model. Achieving a 95% accuracy and 94% F1-score in predicting various disorders, the model surpasses traditional methods, signifying a significant advancement in disease prediction accuracy. The implications of this research extend beyond the confines of academia., Comment: 16 Pages, 4 Figures

Studying and controlling quantum many-body interactions is fundamentally important for quantum science and related emerging technologies. Optically addressable solid-state spins offer a promising platform for exploring various quantum many-body phenomena due to their scalability to a large Hilbert space. However, it is often challenging to probe many-body dynamics in solid-state spin systems due to large on-site disorder and undesired coupling to the environment. Here, we investigate an optically addressable solid-state spin system comprising a strongly interacting ensemble of millions of ytterbium-171 ions in a crystal. Notably, this platform features a clock transition that gives rise to pure long-range spin-exchange interactions, termed the dipolar XY model. Leveraging this unique feature, we investigate quantum thermalization by varying the relative ratio of interaction strength to disorder, dynamically engineering the XY model into other many-body Hamiltonian models, and realizing a time-crystalline phase of matter through periodic driving. Our findings indicate that an ensemble of rare-earth ions serves as a versatile testbed for many-body physics and offers valuable insights for advancing quantum technologies.

Observations of low-mass protostellar systems show evidence of rich complex organic chemistry. Their low luminosity, however, makes determining abundance distributions of complex organic molecules (COMs) within the water snowline challenging. However, the excitation conditions sampled by differing molecular distributions may produce substantive changes in the resulting emission. Thus, molecular excitation may recover spatial information from spatially unresolved data. By analyzing spatially-unresolved NOrthern Extended Millimeter Array (NOEMA) observations of CH$_3$OH and CH$_3$CN, we aim to determine if CH$_3$OH and CH$_3$CN are distributed differently in the protostellar disk around HOPS-370, a highly-luminous intermediate mass protostar. Rotational diagram analysis of CH$_3$OH and CH$_3$CN yields rotational temperatures of $198 \pm 1.2$ K and $448 \pm 19$ K, respectively, suggesting the two molecules have different spatial distributions. Source-specific 3D LTE radiative transfer models are used to constrain the spatial distribution of CH$_3$OH and CH$_3$CN within the disk. A uniform distribution with an abundance of $4\times10^{-8}$ reproduces the CH$_3$OH observations. In contrast, the spatial distribution of CH$_3$CN needs to be either more compact (within $\sim120$ au versus $\sim240$ au for CH$_3$OH) or exhibiting a factor of $\gtrsim 15$ increase in abundance in the inner $\sim55$ au. A possible explanation for the difference in spatial abundance distributions of CH$_3$OH and CH$_3$CN is carbon-grain sublimation., Comment: Accepted for publication in ApJ: 18 pages, 9 figures, 4 tables

TOI-6255~b (GJ 4256) is an Earth-sized planet (1.079$\pm0.065$ $R_\oplus$) with an orbital period of only 5.7 hours. With the newly commissioned Keck Planet Finder (KPF) and CARMENES spectrographs, we determined the planet's mass to be 1.44$\pm$0.14 $M_{\oplus}$. The planet is just outside the Roche limit, with $P_{\rm orb}/P_{\rm Roche}$ = 1.13 $\pm0.10$. The strong tidal force likely deforms the planet into a triaxial ellipsoid with a long axis that is $\sim$10\% longer than the short axis. Assuming a reduced stellar tidal quality factor $Q_\star^\prime \approx10^7$, we predict that tidal orbital decay will cause TOI-6255 to reach the Roche limit in roughly 400 Myr. Such tidal disruptions may produce the possible signatures of planet engulfment that have been on stars with anomalously high refractory elemental abundances compared to its conatal binary companion. TOI-6255 b is also a favorable target for searching for star-planet magnetic interactions, which might cause interior melting and hasten orbital decay. TOI-6255 b is a top target (Emission Spectroscopy Metric of about 24) for phase curve observations with the James Webb Space Telescope., Comment: 18 pages, 7 figures, 5 tables, accepted to AAS Journals. The first RV mass measurement from the Keck Planet Finder

We measure Au and Ag atomic-size contacts in magnetic fields, from zero field to 20 T. We find a magnetic field induced torque which leads to atomic binding at shorter distances than at zero field. Furthermore, the conductance drops below $G_0$ at high magnetic fields by about 15\% in Au. We calculate the conduction through nanosized Au contacts containing residual $O_2$ molecules attached to the contact region and find a spin polarized current when $O_2$ is located at the atomic contact. We discuss the role of spin-orbit coupling in the magnetic properties of $O_2$ attached to Au.

The Quasi-Biennial Oscillation (QBO) is the dominant mode of variability in the equatorial stratosphere. It is characterized by alternating descending easterly and westerly jets over a period of approximately 28 months. It has long been known that the QBO interactions with the annual cycle, e.g., through variation in tropical upwelling, leading to variations in the descent rate of the jets and, resultingly, the QBO period. Understanding these interactions, however, has been hindered by the fact that conventional measures of the QBO convolve these interactions. Koopman formalism, derived from dynamical systems, allows one to decompose spatio-temporal datasets (or nonlinear systems) into spatial modes that evolve coherently with distinct frequencies. We use a data-driven approximation of the Koopman operator on zonal-mean zonal-wind to find modes that correspond to the annual cycle, the QBO, and the nonlinear interactions between the two. From these modes, we establish a data-driven index for a "pure" QBO that is independent of the annual cycle and investigate how the annual cycle modulates the QBO. We begin with what is already known, quantifying the Holton-Tan effect, a nonlinear interaction between the QBO and the annual cycle of the polar stratospheric vortex. We then use the pure QBO to do something new, quantifying how the annual cycle changes the descent rate of the QBO, revealing annual variations with amplitudes comparable to the $30 \, \mathrm{m} \, \mathrm{day}^{-1}$ mean descent rate. We compare these results to the annual variation in tropical upwelling and interpret then with a simple model., Comment: 16 pages, 13 figures

The massive binary HDE 228766 is composed of an O type primary and an evolved secondary. However, previous qualitative analyses of the composite spectrum have led to a wide discussion about whether the secondary is an Of or a Wolf-Rayet star. We use new observations and our novel QER20 package to disentangle for the first time the spectra of the two stellar components and obtain artefact free reconstructed spectra, yielding the more accurate and reliable spectral classifications of O7.5 V((f))z for the primary and O6 Iaf for the secondary. The emission features of the P-Cygni profiles of the H\b{eta} and He I 5876 {\AA} lines, present in the reconstructed spectrum of the secondary, show that this star is at an initial phase of its transition to the WN evolutionary stage. A previously unobserved variable emission, composed of at least four independent features, is seen since 2014 superposed to the H{\gamma} absorption line. Our analysis reveals that these emission features originate from a physically extended region. This could be explained by an episode of enhanced mass-loss in the scenario of a non conservative evolution of the binary.

Photonic graph states are important for measurement- and fusion-based quantum computing, quantum networks, and sensing. They can in principle be generated deterministically by using emitters to create the requisite entanglement. Finding ways to minimize the number of entangling gates between emitters and understanding the overall optimization complexity of such protocols is crucial for practical implementations. Here, we address these issues using graph theory concepts. We develop optimizers that minimize the number of entangling gates, reducing them by up to 75$\%$ compared to naive schemes for moderately sized random graphs. While the complexity of optimizing emitter-emitter CNOT counts is likely NP-hard, we are able to develop heuristics based on strong connections between graph transformations and the optimization of stabilizer circuits. These patterns allow us to process large graphs and still achieve a reduction of up to $66\%$ in emitter CNOTs, without relying on subtle metrics such as edge density. We find the optimal emission orderings and circuits to prepare unencoded and encoded repeater graph states of any size, achieving global minimization of emitter and CNOT resources despite the average NP-hardness of both optimization problems. We further study the locally equivalent orbit of graphs. Although enumerating orbits is $\#$P complete for arbitrary graphs, we analytically calculate the size of the orbit of repeater graphs and find a procedure to generate the orbit for any repeater size. Finally, we inspect the entangling gate cost of preparing any graph from a given orbit and show that we can achieve the same optimal CNOT count across the orbit., Comment: 41 pages, 29 figures

SqlCompose brings generative AI into the data analytics domain. SQL is declarative, has formal table schemas, and is often written in a non-linear manner. We address each of these challenges and develop a set of models that shows the importance of each problem. We first develop an internal SQL benchmark to perform offline tests at Meta. We evaluate how well the Public Llama model performs. We attain a BLEU score of 53% and 24% for single- and multi-line predictions, respectively. This performance is consistent with prior works on imperative languages. We then fine-tune Llama on our internal data and database schemas. SqlComposeSA substantially outperforms Llama by 16 percentage points on BLEU score. SQL is often written with multiple sub queries and in a non-sequential manner. We develop SqlComposeFIM which is aware of the context before and after the line(s) that need to be completed. This fill-in-the-middle model outperform SqlComposeFIM by 35 percentage points. We also measure how often the models get the correct table names, and SqlComposeFIM is able to do this 75% of the time. Aside from our scientific research, we also roll out SqlComposeFIM at Meta. SqlCompose is used on a weekly basis by over 10k users including data scientists and software engineers, less than 1% of users have disabled SqlCompose. We use the feedback from users to improve SqlCompose. Interesting positive themes include completing tedious or repetitive SQL clauses, suggesting boilerplate coding, and help in eliminate the need to remember difficult SQL syntax. The most significant negative themes was table and column name hallucinations, which has been reduced with the release of SqlComposeFIM. The SqlCompose models consistently outperform public and internal LLMs, despite being smaller (7 bn and 13 bn), which provides early indications that smaller specialist models can outperform larger general purpose models., Comment: 11 pages

Fine-grained recognition involves the classification of images from subordinate macro-categories, and it is challenging due to small inter-class differences. To overcome this, most methods perform discriminative feature selection enabled by a feature extraction backbone followed by a high-level feature refinement step. Recently, many studies have shown the potential behind vision transformers as a backbone for fine-grained recognition, but their usage of its attention mechanism to select discriminative tokens can be computationally expensive. In this work, we propose a novel and computationally inexpensive metric to identify discriminative regions in an image. We compare the similarity between the global representation of an image given by the CLS token, a learnable token used by transformers for classification, and the local representation of individual patches. We select the regions with the highest similarity to obtain crops, which are forwarded through the same transformer encoder. Finally, high-level features of the original and cropped representations are further refined together in order to make more robust predictions. Through extensive experimental evaluation we demonstrate the effectiveness of our proposed method, obtaining favorable results in terms of accuracy across a variety of datasets. Furthermore, our method achieves these results at a much lower computational cost compared to the alternatives. Code and checkpoints are available at: \url{https://github.com/arkel23/GLSim}., Comment: Main: 12 pages, 5 figures, 5 tables. Appendix: 9 pages, 9 figures, 10 tables. Total: 21 pages, 14 figures, 15 tables

We present an automated framework for solidifying the cohesion between software specifications, their dependently typed models, and implementation at compile time. Model Checking and type checking are currently separate techniques for automatically verifying the correctness of programs. Using Property Based Testing (PBT), Indexed State Monads (ISMs), and dependent types, we are able to model several interesting systems and network protocols, have the type checker verify that our implementation behaves as specified, and test that our model matches the specification's semantics; a step towards combining model and type checking., Comment: Accepted for the Proceedings of the 9th ACM SIGPLAN International Workshop on Type-Driven Development (TyDe '24), September 6, 2024, Milan, Italy

Mars' tadpole craters are small, young craters whose crater rims are incised by one or more exit breaches but lack visible inlets. The tadpole forming climate records the poorly understood drying of Mars since the Early Hesperian. A third of tadpole craters have multiple breaches, therefore a process is needed that was able to generate crater rim incision in multiple locations. We use HiRISE data for four multiple breach tadpole craters to measure their crater fill, rims, and exit breaches. We compare these measurements and other data to our calculations of liquid water supply by rain, surface melting, groundwater discharge, and basal ice sheet melting to discriminate between four proposed formation hypotheses for tadpole breaches, favoring scenarios with ice-filled craters and supraglacial melting. We conclude that multiple breach tadpole craters record hundreds of meters of mid-latitude ice and climate conditions enabling intermittent melting in the Late Hesperian and Amazonian, suggesting that liquid water on Mars has been available in association with water ice for billions of years., Comment: Planetary Science Journal, in press

Disordered hyperuniform materials are very promising for applications but the successful route for synthesizing them requires to understand the interactions induced by the host media that can switch off this hidden order. With this aim we study the model system of vortices in the $\beta$-Bi$_2$Pd superconductor where correlated defects seem to play a determinant role for the nucleation of a gel vortex phase at low densities. We directly image vortices in extended fields-of-view and show that the disordered vortex structure in this material is anti-hyperuniform, contrasting with the case of vortex structures nucleated in samples with point-like disorder. Based on numerical simulations, we show that this anti-hyperuniform structure arises both, from the interaction of a diluted vortex structure with a fourfold-symmetric correlated disorder quite likely generated when cleaving the samples and from the out of equilibrium nature of the quenched configuration., Comment: 13 pages, 10 figures

Automated Essay Scoring (AES) holds significant promise in the field of education, helping educators to mark larger volumes of essays and provide timely feedback. However, Arabic AES research has been limited by the lack of publicly available essay data. This study introduces AR-AES, an Arabic AES benchmark dataset comprising 2046 undergraduate essays, including gender information, scores, and transparent rubric-based evaluation guidelines, providing comprehensive insights into the scoring process. These essays come from four diverse courses, covering both traditional and online exams. Additionally, we pioneer the use of AraBERT for AES, exploring its performance on different question types. We find encouraging results, particularly for Environmental Chemistry and source-dependent essay questions. For the first time, we examine the scale of errors made by a BERT-based AES system, observing that 96.15 percent of the errors are within one point of the first human marker's prediction, on a scale of one to five, with 79.49 percent of predictions matching exactly. In contrast, additional human markers did not exceed 30 percent exact matches with the first marker, with 62.9 percent within one mark. These findings highlight the subjectivity inherent in essay grading, and underscore the potential for current AES technology to assist human markers to grade consistently across large classes.

This article presents novel numerical algorithms based on pseudodifferential operators ($\Psi$DO) for fast, direct solution of the Helmholtz equation in one-, two- and three-dimensional inhomogeneous unbounded media. The proposed approach relies on an Operator Fourier Transform (OFT) representation of $\Psi$DO which frame the problem of computing the inverse Helmholtz operator, with a spatially-dependent wave speed, in terms of two sequential applications of an inverse square root $\Psi$DO. The OFT representation of the action of the square root $\Psi$DO, in turn, can be effected as a superposition of solutions of a pseudo-temporal initial-boundary-value problem for a paraxial equation. The OFT framework offers several advantages over traditional direct and iterative approaches for the solution of the Helmholtz equation. The operator integral transform is amenable to standard quadrature methods and the required pseudo-temporal paraxial equation solutions can be obtained using any suitable numerical method. A specialized quadrature is derived to evaluate the OFT efficiently and an alternating direction implicit method, used in conjunction with standard finite differences, is used to solve the requisite component paraxial equation problems. Numerical studies, in 1, 2, and 3 spatial dimensions, are presented to confirm the expected OFT-based Helmholtz solver convergence rate. In addition, the efficiency and versatility of our proposed approach is demonstrated by tackling nontrivial wave propagation problems, including 2D plane wave scattering from a geometrically complex inhomogeneity, 3D scattering from turbulent channel flow and plane wave transmission through a spherically-symmetric gradient-index acoustic lens. All computations, even the latter lens problem which involves solving the Helmholtz equation with more than one billion complex unknowns, are performed in a single workstation., Comment: 26 pages, 4 figures

Adaptive variational quantum algorithms arguably offer the best prospects for quantum advantage in the NISQ era. Since the inception of the first such algorithm, ADAPT-VQE, many improvements have appeared in the literature. We combine the key improvements along with a novel operator pool -- which we term Coupled Exchange Operator (CEO) pool -- to assess the cost of running state-of-the-art ADAPT-VQE on hardware in terms of measurement counts and circuit depth. We show a dramatic reduction of these quantum resources compared to the early versions of the algorithm. We also find that our state-of-the-art CEO-ADAPT-VQE outperforms UCCSD, the most widely regarded static VQE ansatz, in all relevant metrics.

The intersection of cloud computing, blockchain technology, and the impending era of quantum computing presents a critical juncture for data security. This research addresses the escalating vulnerabilities by proposing a comprehensive framework that integrates Quantum Key Distribution (QKD), CRYSTALS Kyber, and Zero-Knowledge Proofs (ZKPs) for securing data in cloud-based blockchain systems. The primary objective is to fortify data against quantum threats through the implementation of QKD, a quantum-safe cryptographic protocol. We leverage the lattice-based cryptographic mechanism, CRYSTALS Kyber, known for its resilience against quantum attacks. Additionally, ZKPs are introduced to enhance data privacy and verification processes within the cloud and blockchain environment. A significant focus of this research is the performance evaluation of the proposed framework. Rigorous analyses encompass encryption and decryption processes, quantum key generation rates, and overall system efficiency. Practical implications are scrutinized, considering factors such as file size, response time, and computational overhead. The evaluation sheds light on the framework's viability in real-world cloud environments, emphasizing its efficiency in mitigating quantum threats. The findings contribute a robust quantum-safe and ZKP-integrated security framework tailored for cloud-based blockchain storage. By addressing critical gaps in theoretical advancements, this research offers practical insights for organizations seeking to secure their data against quantum threats. The framework's efficiency and scalability underscore its practical feasibility, serving as a guide for implementing enhanced data security in the evolving landscape of quantum computing and blockchain integration within cloud environments., Comment: 15 pages, 5 figures

We propose a Clifford noise reduction (CliNR) scheme that provides a reduction of the logical error rate of Clifford circuit with lower overhead than error correction and without the exponential sampling overhead of error mitigation. CliNR implements Clifford circuits by splitting them into sub-circuits that are performed using gate teleportation. A few random stabilizer measurements are used to detect errors in the resources states consumed by the gate teleportation. This can be seen as a teleported version of the CPC scheme, with offline fault-detection making it scalable. We prove that CliNR achieves a vanishing logical error rate for families of $n$-qubit Clifford circuits with size $s$ such that $nsp^2$ goes to 0, where $p$ is the physical error rate, meaning that it reaches the regime $ns = o(1/p^2)$ whereas the direct implementation is limited to $s = o(1/p)$. Moreover, CliNR uses only $3n+1$ qubits, $2s + o(s)$ gates and has zero rejection rate. This small overhead makes it more practical than quantum error correction in the near term and our numerical simulations show that CliNR provides a reduction of the logical error rate in relevant noise regimes., Comment: 10 pages

In our study, we first constructed a dataset from the tweets of the top 100 medical influencers with the highest Influencer Score during the COVID-19 pandemic. This dataset was then used to construct a socio-semantic network, mapping both their identities and key topics, which are crucial for understanding their impact on public health discourse. To achieve this, we developed a few-shot multi-label classifier to identify influencers and their network actors' identities, employed BERTopic for extracting thematic content, and integrated these components into a network model to analyze their impact on health discourse. To ensure the reproducibility of our results, we have made the code available at https://github.com/ZhijinGuo/Medinfluencer., Comment: ACM SIGKDD 2024 Workshop epiDAMIK 2024: The 7th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery

Millimeter wavelength observations of Class II protoplanetary disks often display strong emission from hydrocarbons and high CS/SO values, providing evidence that the gas-phase C/O ratio commonly exceeds 1 in their outer regions. We present new NOEMA observations of CS $5-4$, SO $7_6-6_5$ and $5_6-4_5$, C$_2$H $N=3-2$, HCN $3-2$, HCO$^+$ $3-2$, and H$^{13}$CO$^+$ $3-2$ in the DR Tau protoplanetary disk at a resolution of $\sim0.4''$ (80 au). Estimates for the disk-averaged CS/SO ratio range from $\sim0.4-0.5$, the lowest value reported thus far for a T Tauri disk. At a projected separation of $\sim180$ au northeast of the star, the SO moment maps exhibit a clump that has no counterpart in the other lines, and the CS/SO value decreases to $<0.2$ at its location. Thermochemical models calculated with DALI indicate that DR Tau's low CS/SO ratio and faint C$_2$H emission can be explained by a gas-phase C/O ratio that is $<1$ at the disk radii traced by NOEMA. Comparisons of DR Tau's SO emission to maps of extended structures traced by $^{13}$CO suggest that late infall may contribute to driving down the gas-phase C/O ratio of its disk., Comment: Accepted by ApJ

We consider Hoffman's program about the limit points of the spectral radius of the Hermitian adjacency matrix of mixed graphs. In particular, we determine all mixed graphs without negative $4$-cycle whose spectral radius does not exceed $\sqrt{2+\sqrt{5}}$, and identify all limit points of spectral radii of mixed graphs., Comment: 17 pages, 12 figures

Ionization drives important chemical and dynamical processes within protoplanetary disks, including the formation of organics and water in the cold midplane and the transportation of material via accretion and magneto-hydrodynamic (MHD) flows. Understanding these ionization-driven processes is crucial for understanding disk evolution and planet formation. We use new and archival ALMA observations of HCO+, H13CO+, and N2H+ to produce the first forward-modeled 2D ionization constraints for the DM Tau protoplanetary disk. We include ionization from multiple sources and explore the disk chemistry under a range of ionizing conditions. Abundances from our 2D chemical models are post-processed using non-LTE radiative transfer, visibility sampling, and imaging, and are compared directly to the observed radial emission profiles. The observations are best fit by a modestly reduced CR ionization rate ($\zeta_{CR}$ ~ 10$^{-18}$ s$^{-1}$) and a hard X-ray spectrum (hardness ratio [HR] = 0.3), which we associate with stellar flaring conditions. Our best-fit model under-produces emission in the inner disk, suggesting that there may be an additional mechanism enhancing ionization in DM Tau's inner disk. Overall, our findings highlight the complexity of ionization in protoplanetary disks and the need for high resolution multi-line studies., Comment: 18 pages, 12 figures, accepted to be published in The Astrophysical Journal (June 25, 2024)

Counterfactual explanations of Graph Neural Networks (GNNs) offer a powerful way to understand data that can naturally be represented by a graph structure. Furthermore, in many domains, it is highly desirable to derive data-driven global explanations or rules that can better explain the high-level properties of the models and data in question. However, evaluating global counterfactual explanations is hard in real-world datasets due to a lack of human-annotated ground truth, which limits their use in areas like molecular sciences. Additionally, the increasing scale of these datasets provides a challenge for random search-based methods. In this paper, we develop a novel global explanation model RLHEX for molecular property prediction. It aligns the counterfactual explanations with human-defined principles, making the explanations more interpretable and easy for experts to evaluate. RLHEX includes a VAE-based graph generator to generate global explanations and an adapter to adjust the latent representation space to human-defined principles. Optimized by Proximal Policy Optimization (PPO), the global explanations produced by RLHEX cover 4.12% more input graphs and reduce the distance between the counterfactual explanation set and the input set by 0.47% on average across three molecular datasets. RLHEX provides a flexible framework to incorporate different human-designed principles into the counterfactual explanation generation process, aligning these explanations with domain expertise. The code and data are released at https://github.com/dqwang122/RLHEX., Comment: Accepted by KDD 2024

We present ALMA Band 7 molecular line observations of the protostars within the VLA 1623 system. We map C$^{17}$O (3 - 2) in the circumbinary disk around VLA 1623A and the outflow cavity walls of the collimated outflow. We further detect red-shifted and blue-shifted velocity gradients in the circumstellar disks around VLA 1623B and VLA 1623W that are consistent with Keplerian rotation. We use the radiative transfer modeling code, pdspy, and simple flared disk models to measure stellar masses of $0.27 \pm 0.03$ M$_\odot$, $1.9^{+0.3}_{-0.2}$ M$_\odot$, and $0.64 \pm 0.06$ M$_\odot$ for the VLA 1623A binary, VLA 1623B, and VLA 1623W, respectively. These results represent the strongest constraints on stellar mass for both VLA 1623B and VLA 1623W, and the first measurement of mass for all stellar components using the same tracer and methodology. We use these masses to discuss the relationship between the young stellar objects (YSOs) in the VLA 1623 system. We find that VLA 1623W is unlikely to be an ejected YSO, as has been previously proposed. While we cannot rule out that VLA 1623W is a unrelated YSO, we propose that it is a true companion star to the VLA 1623A/B system and that the these stars formed in situ through turbulent fragmentation and have had only some dynamical interactions since their inception., Comment: Accepted to A&A; 16 pages, 12 figures

The gas and solid-state C/O ratios provide context to potentially link the atmospheric composition of planets to that of the natal disk. We provide a synthesis of extant estimates of the gaseous C/O and C/H ratios in planet-forming disks obtained primarily through analysis of Atacama Large Millimeter Array observations. These estimates are compared to atmospheric abundances of wide separation (> 10 au) gas giants. The resolved disk gas C/O ratios, from seven systems, generally exhibit C/O > 1 with sub-solar, or depleted, carbon content. In contrast, wide separation gas giants have atmospheric C/O ratios that cluster near or slightly above the presumed stellar value with a range of elemental C/H. From the existing disk composition, we infer that the solid-state mm/cm-sized pebbles have a total C/O ratio (solid cores and ices) that is solar (stellar) in content. We explore simple models that reconstruct the exoplanet atmospheric composition from the disk, while accounting for silicate cloud formation in the planet atmosphere. If wide separate planets formed via the core-accretion mechanism, they must acquire their metals from pebble or planetesimal accretion. Further, the dispersion in giant planet C/H content is best matched by a disk composition with modest and variable factors of carbon depletion. An origin of the wide separation gas giants via gravitational instability cannot be ruled out as stellar C/O ratios should natively form in this scenario. However, the variation in planet metallicity with a stellar C/O ratio potentially presents challenges to these models., Comment: 17 pages, 6 figures, accepted by Astrophysical Journal Letters

Generative models are trained with the simple objective of imitating the conditional probability distribution induced by the data they are trained on. Therefore, when trained on data generated by humans, we may not expect the artificial model to outperform the humans on their original objectives. In this work, we study the phenomenon of transcendence: when a generative model achieves capabilities that surpass the abilities of the experts generating its data. We demonstrate transcendence by training an autoregressive transformer to play chess from game transcripts, and show that the trained model can sometimes achieve better performance than all players in the dataset. We theoretically prove that transcendence can be enabled by low-temperature sampling, and rigorously assess this claim experimentally. Finally, we discuss other sources of transcendence, laying the groundwork for future investigation of this phenomenon in a broader setting., Comment: Code, models, and data at https://transcendence.eddie.win

Standing as one of the most significant barriers to reaching quantum advantage, state-preparation fidelities on noisy intermediate-scale quantum processors suffer from quantum-gate errors, which accumulate over time. A potential remedy is pulse-based state preparation. We numerically investigate the minimal evolution times (METs) attainable by optimizing (microwave and exchange) pulses on silicon hardware. We investigate two state preparation tasks. First, we consider the preparation of molecular ground states and find the METs for H$_2$, HeH$^+$, and LiH to be 2.4 ns, 4.4 ns, and 27.2 ns, respectively. Second, we consider transitions between arbitrary states and find the METs for transitions between arbitrary four-qubit states to be below 50 ns. For comparison, connecting arbitrary two-qubit states via one- and two-qubit gates on the same silicon processor requires approximately 200 ns. This comparison indicates that pulse-based state preparation is likely to utilize the coherence times of silicon hardware more efficiently than gate-based state preparation. Finally, we quantify the effect of silicon device parameters on the MET. We show that increasing the maximal exchange amplitude from 10 MHz to 1 GHz accelerates the METs, e.g., for H$_2$ from 84.3 ns to 2.4 ns. This demonstrates the importance of fast exchange. We also show that increasing the maximal amplitude of the microwave drive from 884 kHz to 56.6 MHz shortens state transitions, e.g., for two-qubit states from 1000 ns to 25 ns. Our results bound both the state-preparation times for general quantum algorithms and the execution times of variational quantum algorithms with silicon spin qubits., Comment: 9 + (7) pages, 6 figs, comments are welcomed

Quantile estimation and regression within the Bayesian framework is challenging as the choice of likelihood and prior is not obvious. In this paper, we introduce a novel Bayesian nonparametric method for quantile estimation and regression based on the recently introduced martingale posterior (MP) framework. The core idea of the MP is that posterior sampling is equivalent to predictive imputation, which allows us to break free of the stringent likelihood-prior specification. We demonstrate that a recursive estimate of a smooth quantile function, subject to a martingale condition, is entirely sufficient for full nonparametric Bayesian inference. We term the resulting posterior distribution as the quantile martingale posterior (QMP), which arises from an implicit generative predictive distribution. Associated with the QMP is an expedient, MCMC-free and parallelizable posterior computation scheme, which can be further accelerated with an asymptotic approximation based on a Gaussian process. Furthermore, the well-known issue of monotonicity in quantile estimation is naturally alleviated through increasing rearrangement due to the connections to the Bayesian bootstrap. Finally, the QMP has a particularly tractable form that allows for comprehensive theoretical study, which forms a main focus of the work. We demonstrate the ease of posterior computation in simulations and real data experiments., Comment: 29 pages (main), 84 pages (total), 12 figures

We demonstrate coherent control of a three-electron exchange-only spin qubit with the quantum dots arranged in a close-packed triangular geometry. The device is tuned to confine one electron in each quantum dot, as evidenced by pairwise charge stability diagrams. Time-domain control of the exchange coupling is demonstrated and qubit performance is characterized using blind randomized benchmarking, with an average single-qubit gate fidelity F = 99.84%. The compact triangular device geometry can be readily scaled to larger two-dimensional quantum dot arrays with high connectivity.

Bridging the gap between internal and external validity is crucial for heterogeneous treatment effect estimation. Randomised controlled trials (RCTs), favoured for their internal validity due to randomisation, often encounter challenges in generalising findings due to strict eligibility criteria. Observational studies on the other hand, provide external validity advantages through larger and more representative samples but suffer from compromised internal validity due to unmeasured confounding. Motivated by these complementary characteristics, we propose a novel Bayesian nonparametric approach leveraging multi-task Gaussian processes to integrate data from both RCTs and observational studies. In particular, we introduce a parameter which controls the degree of borrowing between the datasets and prevents the observational dataset from dominating the estimation. The value of the parameter can be either user-set or chosen through a data-adaptive procedure. Our approach outperforms other methods in point predictions across the covariate support of the observational study, and furthermore provides a calibrated measure of uncertainty for the estimated treatment effects, which is crucial when extrapolating. We demonstrate the robust performance of our approach in diverse scenarios through multiple simulation studies and a real-world education randomised trial.