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Findings of this study indicate that preservice teachers engaged in student teaching tend to become less humanistic and more authoritarian in their relations with students as the student teaching experience progresses. In addition, inner-city student teachers tend to be more custodial before they begin student teaching than are suburban student teachers after they complete student teaching. The sample for the study consisted of fifty-eight secondary school student teachers in suburban schools and twenty-seven secondary student teachers in inner-city schools. Subjects were administered the Pupil Control Ideology Inventory Questionnaire during prestudent teaching orientation and again during the last week of student teaching. Survey results indicate that student teachers tend to enter student teaching with an idealistic and sometimes erroneous concept of what is involved regarding discipline maintenance in the classroom, thus undergoing a marked change toward more dominant classroom control forms as the experience progresses. The more stringent screening process for student teachers in inner-city schools appears to account in part for the finding that these student teachers are more custodial before they begin student teaching than are suburban student teachers after they complete student teaching. (MB)

Mental health concerns are prevalent among college students, highlighting the need for effective interventions that promote self-awareness and holistic well-being. MindScape pioneers a novel approach to AI-powered journaling by integrating passively collected behavioral patterns such as conversational engagement, sleep, and location with Large Language Models (LLMs). This integration creates a highly personalized and context-aware journaling experience, enhancing self-awareness and well-being by embedding behavioral intelligence into AI. We present an 8-week exploratory study with 20 college students, demonstrating the MindScape app's efficacy in enhancing positive affect (7%), reducing negative affect (11%), loneliness (6%), and anxiety and depression, with a significant week-over-week decrease in PHQ-4 scores (-0.25 coefficient), alongside improvements in mindfulness (7%) and self-reflection (6%). The study highlights the advantages of contextual AI journaling, with participants particularly appreciating the tailored prompts and insights provided by the MindScape app. Our analysis also includes a comparison of responses to AI-driven contextual versus generic prompts, participant feedback insights, and proposed strategies for leveraging contextual AI journaling to improve well-being on college campuses. By showcasing the potential of contextual AI journaling to support mental health, we provide a foundation for further investigation into the effects of contextual AI journaling on mental health and well-being., Comment: arXiv admin note: text overlap with arXiv:2404.00487

Recent work has documented striking heterogeneity in the performance of state-of-the-art vision language models (VLMs), including both multimodal language models and text-to-image models. These models are able to describe and generate a diverse array of complex, naturalistic images, yet they exhibit surprising failures on basic multi-object reasoning tasks -- such as counting, localization, and simple forms of visual analogy -- that humans perform with near perfect accuracy. To better understand this puzzling pattern of successes and failures, we turn to theoretical accounts of the binding problem in cognitive science and neuroscience, a fundamental problem that arises when a shared set of representational resources must be used to represent distinct entities (e.g., to represent multiple objects in an image), necessitating the use of serial processing to avoid interference. We find that many of the puzzling failures of state-of-the-art VLMs can be explained as arising due to the binding problem, and that these failure modes are strikingly similar to the limitations exhibited by rapid, feedforward processing in the human brain.

Parameterized artificial neural networks (ANNs) can be very expressive ansatzes for variational algorithms, reaching state-of-the-art energies on many quantum many-body Hamiltonians. Nevertheless, the training of the ANN can be slow and stymied by the presence of local minima in the parameter landscape. One approach to mitigate this issue is to use parallel tempering methods, and in this work we focus on the role played by the temperature distribution of the parallel tempering replicas. Using an adaptive method that adjusts the temperatures in order to equate the exchange probability between neighboring replicas, we show that this temperature optimization can significantly increase the success rate of the variational algorithm with negligible computational cost by eliminating bottlenecks in the replicas' random walk. We demonstrate this using two different neural networks, a restricted Boltzmann machine and a feedforward network, which we use to study a toy problem based on a permutation invariant Hamiltonian with a pernicious local minimum and the J1-J2 model on a rectangular lattice.

In this PhD thesis we investigate stellar evolution and nucleosynthesis in the low- and extremely-low metallicity regime - including models of stars with a pure Big Bang composition (i.e. $\rm{Z} = 0$). The metallicity range of the extremely metal-poor (EMP) models calculated is $-6.5 < \rm{[Fe/H]} < -3.0$, with a mass range $0.85 < \rm{M} < 3.0~\rm{M}_{\odot}$. We have also calculated a series of models with a metallicity of $\rm{[Fe/H]} = -1.4$, to compare with observations of abundance patterns in Galactic globular cluster stars. Many of the extremely metal-poor (EMP) and $\rm{Z} = 0$ models experience violent evolutionary episodes not seen at higher metallicities. We refer to these events as `Dual Flashes' (DF) since they are characterised by peaks in the hydrogen and helium burning luminosities occurring at the same time. Some of the material processed by these events is later dredged up by the convective envelope, causing very significant surface pollution. We have calculated the entire evolution of the $\rm{Z} = 0$ and EMP models, including detailed nucleosynthesis and yields. Although subject to many uncertainties these are, as far as we are aware, the only yields available in this mass and metallicity range. We find that our models predict an increased number of carbon-rich stars at the lowest metallicities. This is mainly due to the extra pollution provided by the DF events - which do not occur in higher metallicity models. This concurs well with the observations that show the proportion of carbon-enhanced metal-poor (CEMP) stars in the Galactic Halo to be higher at lower metallicities. We also compare the chemical pollution arising from our models with the detailed abundance patterns available for some of the most metal-poor CEMP stars, and find mixed results. Fluid dynamics calculations are likely needed to model the violent DF episodes. [Abridged], Comment: PhD thesis, conferred 2008, 428 pages. Monash University, Australia

Scene understanding is a fundamental capability needed in many domains, ranging from question-answering to robotics. Unlike recent end-to-end approaches that must explicitly learn varying compositions of the same scene, our method reasons over their constituent objects and analyzes their arrangement to infer a scene's meaning. We propose a novel approach that reasons over a scene's scene- and knowledge-graph, capturing spatial information while being able to utilize general domain knowledge in a joint graph search. Empirically, we demonstrate the feasibility of our method on the ADE20K dataset and compare it to current scene understanding approaches.

We prove conjectures of Garoufalidis-Gu-Mari\~no that perturbative series associated with the hyperbolic knots $4_1$ and $5_2$ are resurgent and Borel summable. In the process, we give an algorithm that can be used to explicitly compute the Borel-Laplace resummation as a combination of state integrals of Andersen-Kashaev. This gives a complete description of the resurgent structure in these examples and allows for explicit computations of Stokes constants., Comment: 44 pages, 32 figures

The evolution of protoplanetary disks in regions with massive OB stars is influenced by externally driven winds that deplete the outer parts of disks. These winds have previously been studied via forbidden oxygen emission lines, which also arise in isolated disks in low-mass star forming-regions (SFRs) with weak external UV fields in photoevaporative or magnetic (internal) disk winds. It is crucial to determine how to disentangle external winds from internal ones. Here, we report a proxy for unambiguously identifying externally driven winds with a forbidden line of neutral atomic carbon, [C i] 8727 A. We compare for the first time the spatial location of the emission in the [O i] 5577 A, [O i] 6300 A, and [C i] 8727 A lines traced by VLT/MUSE-NFM, with the ALMA Band 7 continuum disk emission in a sample of 12 proplyds in the Orion Nebula Cluster (ONC). We confirm that the [O i] 5577 A emission is co-spatial with the disk emission, whereas the [O i] 6300 A is emitted both on the disk surface and on the ionization front of the proplyds. We show for the first time that the [C i] 8727 A line is also co-spatial with the disk surface in proplyds, as seen in the MUSE and ALMA data comparison. To verify whether the [C i] 8727 A line is detected in regions where external photoevaporation is not expected, we examine VLT/X-Shooter spectra for young stars in low-mass SFRs. Although the [O i] lines are well detected in all these targets, there is <<10% detection rate in the case of the [C i] 8727 A line. This number increases substantially to a ~40% detection rate in sigma-Orionis, a region with intermediate UV radiation. The spatial location of the [C i] 8727 A line emission and the lack of its detection in isolated disks in low-mass SFRs strongly suggest that this line is a tell-trace tracer of externally driven photoevaporative winds, which agrees with recent excitation models., Comment: Accepted to A&A on October 25, 2024

The coupling between acoustic vibrations in a lithium niobate bulk acoustic wave resonator and microwave photons of a re-entrant microwave cavity was investigated at a temperature close to 4 K. Coupling was achieved by placing the acoustic resonator in the location of the re-entrant cavity electric field maxima, in a symmetric ``split-post'' configuration, with a large overlap between the microwave field and the acoustic mode, allowing acoustic modulations of the microwave frequency. We show that the acoustic modes in this setup retain large inherent quality factors of greater than $10^6$. A maximum optomechanical coupling rate was determined to be $g_0$ = 0.014 mHz, four orders of magnitude larger than previous results obtained using a quartz BAW at 4 K in a similar experimental setup, but using a single post-re-entrant cavity resonator.

We explore red stellar populations toward the W3 giant molecular cloud through the use of optical-to-infrared (IR) photometry and Gaia DR 3 data, simultaneously characterizing stellar content and properties of dust in the molecular medium. We use a Rayleigh-Jeans Color Excess (RJCE) method modified to de-redden stellar observations of both red giants (RGs) and OB stars, and construct an IR Hertzsprung-Russell diagram validated against the Besanccon Galactic model. Taking advantage of the near-universal IR interstellar extinction law and precise Gaia measurements, we develop a method for obtaining the spectral classification, foreground extinction, and distance moduli of stars, validated by spectroscopically-confirmed OB stars. We constrain the observed parallax and proper motion of OB stars in W3, demonstrating the importance of considering systematic effects in the parallax bias, and assign parallax- and proper motion-based cloud membership to our stellar samples. While it has been assumed that all spectroscopic OB stars are inside the W3 cloud, we find evidence of seven background B stars and three potential runaway OB stars. The methods developed here based on known stellar populations enable us to identify 82 new OB candidates that are confidently within the cloud. We quantify several dust-to-dust empirical correlations, in particular the IR color excess $E(H-[4.5])$ and the optical depth $\tau_1$ of submillimeter dust emission at 1 THz using RGs behind W3, measuring a best fit of $E(H-[4.5]) = (1.07 \pm 0.04) \times 10^3 \, \tau_{1,\,\mathrm{HOTT}} + (0.00 \pm 0.02)$ mags., Comment: accepted for publication in ApJ, 39 pages, 20 figures

Energy arbitrage is one of the most profitable sources of income for battery operators, generating revenues by buying and selling electricity at different prices. Forecasting these revenues is challenging due to the inherent uncertainty of electricity prices. Deep reinforcement learning (DRL) emerged in recent years as a promising tool, able to cope with uncertainty by training on large quantities of historical data. However, without access to future electricity prices, DRL agents can only react to the currently observed price and not learn to plan battery dispatch. Therefore, in this study, we combine DRL with time-series forecasting methods from deep learning to enhance the performance on energy arbitrage. We conduct a case study using price data from Alberta, Canada that is characterized by irregular price spikes and highly non-stationary. This data is challenging to forecast even when state-of-the-art deep learning models consisting of convolutional layers, recurrent layers, and attention modules are deployed. Our results show that energy arbitrage with DRL-enabled battery control still significantly benefits from these imperfect predictions, but only if predictors for several horizons are combined. Grouping multiple predictions for the next 24-hour window, accumulated rewards increased by 60% for deep Q-networks (DQN) compared to the experiments without forecasts. We hypothesize that multiple predictors, despite their imperfections, convey useful information regarding the future development of electricity prices through a "majority vote" principle, enabling the DRL agent to learn more profitable control policies., Comment: Accepted for publication at the 18th ASME International Conference on Energy Sustainability

GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50\% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models. In line with our commitment to building AI safely and consistent with our voluntary commitments to the White House, we are sharing the GPT-4o System Card, which includes our Preparedness Framework evaluations. In this System Card, we provide a detailed look at GPT-4o's capabilities, limitations, and safety evaluations across multiple categories, focusing on speech-to-speech while also evaluating text and image capabilities, and measures we've implemented to ensure the model is safe and aligned. We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.

Treewidth and Hadwiger number are two of the most important parameters in structural graph theory. This paper studies graph classes in which large treewidth implies the existence of a large complete graph minor. To formalise this, we say that a graph class $\mathcal{G}$ is (tw,had)-bounded if there is a function $f$ (called the (tw,had)-bounding function) such that tw$(G)$ $\leq$ $f$(had$(G)$) for every graph $G \in \mathcal{G}$. We characterise (tw,had)-bounded graph classes as those that exclude some planar graph as an induced minor, and use this characterisation to show that every proper vertex-minor-closed class is (tw,had)-bounded. Furthermore, we demonstrate that any (tw,had)-bounded graph class has a (tw,had)-bounding function in O(had$(G)^9$polylog(had$(G)$)). Our bound comes from the bound for the Grid Minor Theorem given by Chuzhoy and Tan, and any quantitative improvement to their result will lead directly to an improvement to our result. More strongly, we conjecture that every (tw,had)-bounded graph class has a linear (tw,had)-bounding function. In support of this conjecture, we show that it holds for the class of outer-string graphs, and for a natural generalisation of outer-string graphs: intersection graphs of strings rooted at the boundary of a fixed surface. We also verify our conjecture for low-rank perturbations of circle graphs, which is an important step towards verifying it for all proper vertex-minor-closed classes., Comment: 26 pages, 6 Figures

A Bayesian coreset is a small, weighted subset of a data set that replaces the full data during inference to reduce computational cost. The state-of-the-art coreset construction algorithm, Coreset Markov chain Monte Carlo (Coreset MCMC), uses draws from an adaptive Markov chain targeting the coreset posterior to train the coreset weights via stochastic gradient optimization. However, the quality of the constructed coreset, and thus the quality of its posterior approximation, is sensitive to the stochastic optimization learning rate. In this work, we propose a learning-rate-free stochastic gradient optimization procedure, Hot-start Distance over Gradient (Hot DoG), for training coreset weights in Coreset MCMC without user tuning effort. Empirical results demonstrate that Hot DoG provides higher quality posterior approximations than other learning-rate-free stochastic gradient methods, and performs competitively to optimally-tuned ADAM.

Many common Markov chain Monte Carlo (MCMC) kernels can be formulated using a deterministic involutive proposal with a step size parameter. Selecting an appropriate step size is often a challenging task in practice; and for complex multiscale targets, there may not be one choice of step size that works well globally. In this work, we address this problem with a novel class of involutive MCMC methods -- AutoStep MCMC -- that selects an appropriate step size at each iteration adapted to the local geometry of the target distribution. We prove that AutoStep MCMC is $\pi$-invariant and has other desirable properties under mild assumptions on the target distribution $\pi$ and involutive proposal. Empirical results examine the effect of various step size selection design choices, and show that AutoStep MCMC is competitive with state-of-the-art methods in terms of effective sample size per unit cost on a range of challenging target distributions.

The correct specification of reward models is a well-known challenge in reinforcement learning. Hand-crafted reward functions often lead to inefficient or suboptimal policies and may not be aligned with user values. Reinforcement learning from human feedback is a successful technique that can mitigate such issues, however, the collection of human feedback can be laborious. Recent works have solicited feedback from pre-trained large language models rather than humans to reduce or eliminate human effort, however, these approaches yield poor performance in the presence of hallucination and other errors. This paper studies the advantages and limitations of reinforcement learning from large language model feedback and proposes a simple yet effective method for soliciting and applying feedback as a potential-based shaping function. We theoretically show that inconsistent rankings, which approximate ranking errors, lead to uninformative rewards with our approach. Our method empirically improves convergence speed and policy returns over commonly used baselines even with significant ranking errors, and eliminates the need for complex post-processing of reward functions., Comment: 13 pages, 8 figures, The 2024 Conference on Empirical Methods in Natural Language Processing

We investigate the electronic structure of high-density layers of Sb dopants in a silicon host, so-called Si:Sb $\delta$-layers. We show that, in spite of the known challenges in producing highly confined Sb $\delta$-layers, sufficient confinement is created such that the lowest conduction band states ($\Gamma$ states, studied in depth in other silicon $\delta$-layers), become occupied and can be observed using angle-resolved photoemission spectroscopy. The electronic structure of the Si:Sb $\delta$-layers closely resembles that of Si:P systems, where the observed conduction band is near-parabolic and slightly anisotropic in the $\mathbf{k}_\parallel$ plane. The observed $\Gamma$ state extends ~ 1 nm in the out-of-plane direction, which is slightly wider than the 1/3 monolayer thick dopant distribution. This is caused by a small segregation of the dopant layer, which is nevertheless minimal when comparing with earlier published attempts. Our results serve to demonstrate that Sb is still a feasible dopant alternative for use in the semiconductor $\delta$-layer platform, providing similar electronic functionality to Si:P systems. Additionally, it has the advantages of being less expensive, more controllable, safer to handle, and more compatible with industrial patterning techniques. Si:Sb is therefore a viable platform for emerging quantum device applications.

The Canadian Automated Meteor Observatory (CAMO) mirror tracking system has been in operation since 2009 and has, to date, produced more than 20,000 two-station meteor observations at meter-level spatial and 10 ms temporal resolution. In 2020, a spectral tracking camera was added in parallel at one of the CAMO stations. To date, it has recorded the spectra of hundreds of faint meteors. Engineering testing from 2020-2023 resulted in the selection of a 150 lpmm grating and an EMCCD camera to achieve a spectral resolution of about 1 nm/pixel in the final configuration. The CAMO spectral system can resolve spectra from individual meteoroid fragments, record spectra for meteors of +2 peak magnitude to as faint as +4 in parts of the lightcurve and produce relative abundance estimates for Mg, Fe and Na. Our preliminary results also show identification of the H and K lines of CA(II). Meteors with strong iron lines were found to have unusual fragmentation behaviour, involving gross fragmentation rather than continuously shedding small particles. The spectra of individual fragments can be resolved in some cases, showing that these Fe-rich objects do not differ in composition among fragments. Our calibration procedure and hardware configuration are discussed together with preliminary results., Comment: Accepted (Oct. 22, 2024) for publication in Publications of the Astronomical Society of the Pacific

We report on the masses and hyperfine structure of ground and isomeric states in $^{114,116,118,120}$Ag isotopes, measured with the phase-imaging ion-cyclotron-resonance technique (PI-ICR) with the JYFLTRAP mass spectrometer and the collinear laser spectroscopy beamline at the Ion Guide Isotope Separator On-Line (IGISOL) facility, Jyv\"askyl\"a, Finland. We measured the masses and excitation energies, electromagnetic moments, and charge radii, and firmly established the nuclear spins of the long-lived states. A new isomer was discovered in $^{118}$Ag and the half-lives of $^{118}$Ag long-lived states were reevaluated. We unambiguously pinned down the level ordering of all long-lived states, placing the inversion of the $I = 0^-$ and $I = 4^+$ states at $A = 118$ $(N = 71)$. Lastly, we compared the electromagnetic moments of each state to empirical single-particle moments to identify the dominant configuration where possible., Comment: 11 pages paper (excl. references) + 3 pages of supplementary material

The challenges of operating qubits in a cryogenic environment point to a looming bottleneck for large-scale quantum processors, limited by the number of input-output connections. Classical processors solve this problem via multiplexing; however, on-chip multiplexing circuits have not been shown to have similar benefits for cryogenic quantum devices. In this work we integrate classical circuitry and Si/SiGe quantum devices on the same chip, providing a test bed for qubit scale-up. Our method uses on-chip field-effect transistors (FETs) to multiplex a grid of work zones, achieving a nearly tenfold reduction in control wiring. We leverage this set-up to probe device properties across a 6x6mm$^2$ array of 16 Hall bars. We successfully operate the array at cryogenic temperatures and high magnetic fields where the quantum Hall effect is observed. Building upon these results, we propose a vision for readout in a large-scale silicon quantum processor with a limited number of control connections.

Urgent applications like wildfire management and renewable energy generation require precise, localized weather forecasts near the Earth's surface. However, weather forecast products from machine learning or numerical weather models are currently generated on a global regular grid, on which a naive interpolation cannot accurately reflect fine-grained weather patterns close to the ground. In this work, we train a heterogeneous graph neural network (GNN) end-to-end to downscale gridded forecasts to off-grid locations of interest. This multi-modal GNN takes advantage of local historical weather observations (e.g., wind, temperature) to correct the gridded weather forecast at different lead times towards locally accurate forecasts. Each data modality is modeled as a different type of node in the graph. Using message passing, the node at the prediction location aggregates information from its heterogeneous neighbor nodes. Experiments using weather stations across the Northeastern United States show that our model outperforms a range of data-driven and non-data-driven off-grid forecasting methods. Our approach demonstrates how the gap between global large-scale weather models and locally accurate predictions can be bridged to inform localized decision-making.

We present MultiCamCows2024, a farm-scale image dataset filmed across multiple cameras for the biometric identification of individual Holstein-Friesian cattle exploiting their unique black and white coat-patterns. Captured by three ceiling-mounted visual sensors covering adjacent barn areas over seven days on a working dairy farm, the dataset comprises 101, 329 images of 90 cows, plus the underlying original CCTV footage. The dataset is provided alongside full computer vision recognition baselines, that is both a supervised and self-supervised learning framework for individual cow identification trained on cattle tracklets. We report a performance above 96% single image identification accuracy from the dataset and demonstrate that combining data from multiple cameras during learning enhances self-supervised identification. We show that our framework enables fully automatic cattle identification, barring only the simple human verification of tracklet integrity during data collection. Crucially, our study highlights that multi-camera, supervised and self-supervised components in tandem not only deliver highly accurate individual cow identification but also achieve this efficiently with no labelling of cattle identities by humans at all. We argue that this improvement in efficacy has practical implications for livestock management, behaviour analysis, and agricultural monitoring. For full reproducibility and practical ease of use, we publish all key software and code including re-identification components and the species detector with this paper., Comment: 26 pages, 10 figures

Grading Objective Structured Clinical Examinations (OSCEs) is a time-consuming and expensive process, traditionally requiring extensive manual effort from human experts. In this study, we explore the potential of Large Language Models (LLMs) to assess skills related to medical student communication. We analyzed 2,027 video-recorded OSCE examinations from the University of Texas Southwestern Medical Center (UTSW), spanning four years (2019-2022), and several different medical cases or "stations." Specifically, our focus was on evaluating students' ability to summarize patients' medical history: we targeted the rubric item 'did the student summarize the patients' medical history?' from the communication skills rubric. After transcribing speech audio captured by OSCE videos using Whisper-v3, we studied the performance of various LLM-based approaches for grading students on this summarization task based on their examination transcripts. Using various frontier-level open-source and proprietary LLMs, we evaluated different techniques such as zero-shot chain-of-thought prompting, retrieval augmented generation, and multi-model ensemble methods. Our results show that frontier LLM models like GPT-4 achieved remarkable alignment with human graders, demonstrating a Cohen's kappa agreement of 0.88 and indicating strong potential for LLM-based OSCE grading to augment the current grading process. Open-source models also showed promising results, suggesting potential for widespread, cost-effective deployment. Further, we present a failure analysis identifying conditions where LLM grading may be less reliable in this context and recommend best practices for deploying LLMs in medical education settings.

Can we detect an object that is not visible in an image? This study introduces the novel task of 2D and 3D unobserved object detection for predicting the location of objects that are occluded or lie outside the image frame. We adapt several state-of-the-art pre-trained generative models to solve this task, including 2D and 3D diffusion models and vision--language models, and show that they can be used to infer the presence of objects that are not directly observed. To benchmark this task, we propose a suite of metrics that captures different aspects of performance. Our empirical evaluations on indoor scenes from the RealEstate10k dataset with COCO object categories demonstrate results that motivate the use of generative models for the unobserved object detection task. The current work presents a promising step towards compelling applications like visual search and probabilistic planning that can leverage object detection beyond what can be directly observed., Comment: 16 pages; 41 figures

A universality conjecture of Farmer and Rhoades [Trans. Amer. Math. Soc., 357(9):3789--3811, 2005] and Farmer [Adv. Math., 411:Paper No. 108781, 14, 2022] asserts that, under some natural conditions, the roots of an entire function should become perfectly spaced in the limit of repeated differentiation. This conjecture is known as Cosine Universality. We establish this conjecture for a class of even entire functions with only real roots which are real on the real line. Along the way, we establish a number of additional universality results for Jensen polynomials of entire functions, including the Hermite Universality conjecture of Farmer [Adv. Math., 411:Paper No. 108781, 14, 2022]. Our proofs are based on finite free probability theory. We establish finite free probability analogs of the law of large numbers, central limit theorem, and Poisson limit theorem for sequences of deterministic polynomials under repeated differentiation, under optimal moment conditions, which are of independent interest., Comment: 30 pages, 1 figure

Discrete diffusion has achieved state-of-the-art performance, outperforming or approaching autoregressive models on standard benchmarks. In this work, we introduce Discrete Diffusion with Planned Denoising (DDPD), a novel framework that separates the generation process into two models: a planner and a denoiser. At inference time, the planner selects which positions to denoise next by identifying the most corrupted positions in need of denoising, including both initially corrupted and those requiring additional refinement. This plan-and-denoise approach enables more efficient reconstruction during generation by iteratively identifying and denoising corruptions in the optimal order. DDPD outperforms traditional denoiser-only mask diffusion methods, achieving superior results on language modeling benchmarks such as text8, OpenWebText, and token-based generation on ImageNet $256 \times 256$. Notably, in language modeling, DDPD significantly reduces the performance gap between diffusion-based and autoregressive methods in terms of generative perplexity. Code is available at https://github.com/liusulin/DDPD.

Generalizing a theorem of the first two authors and Geelen for planes, we show that, for a real-representable matroid $M$, either the average hyperplane-size in $M$ is at most a constant depending only on its rank, or each hyperplane of $M$ contains one of a set of at most $r(M)-2$ lines. Additionally, in the latter case, the ground set of $M$ has a partition $(E_{1}, E_{2})$, where $E_{1}$ can be covered by few flats of relatively low rank and $|E_{2}|$ is bounded. Finally, we formulate a high-dimensional generalization of a classic problem of Motzkin, Gr\"unbaum, Erd\H{o}s and Purdy on sets of red and blue points in the plane with no monochromatic blue line. We show that the solution to this problem gives a tight upper bound on $|E_{2}|$. We also discuss this high-dimensional problem in its own right, and prove some initial results.

Quantum error correction (QEC) will be essential to achieve the accuracy needed for quantum computers to realise their full potential. The field has seen promising progress with demonstrations of early QEC and real-time decoded experiments. As quantum computers advance towards demonstrating a universal fault-tolerant logical gate set, implementing scalable and low-latency real-time decoding will be crucial to prevent the backlog problem, avoiding an exponential slowdown and maintaining a fast logical clock rate. Here, we demonstrate low-latency feedback with a scalable FPGA decoder integrated into the control system of a superconducting quantum processor. We perform an 8-qubit stability experiment with up to $25$ decoding rounds and a mean decoding time per round below $1$ ${\mu}s$, showing that we avoid the backlog problem even on superconducting hardware with the strictest speed requirements. We observe logical error suppression as the number of decoding rounds is increased. We also implement and time a fast-feedback experiment demonstrating a decoding response time of $9.6$ ${\mu}s$ for a total of $9$ measurement rounds. The decoder throughput and latency developed in this work, combined with continued device improvements, unlock the next generation of experiments that go beyond purely keeping logical qubits alive and into demonstrating building blocks of fault-tolerant computation, such as lattice surgery and magic state teleportation., Comment: 11 pages, 4 figures, Supplementary Information

If we consider previously introduced extensions of Stanley's chromatic symmetric function $X_{G}(x_1, x_2, \ldots)$ for a graph $G$ to elements in the algebra $\textsf{QSym}$ of quasisymmetric functions and in the algebra $\textsf{NCSym}$ of symmetric functions in noncommuting variables, this motivates our introduction of a lifting of $X_{G}$ to the dual of $\textsf{QSym}$, i.e., the algebra $\textsf{NSym}$ of noncommutative symmetric functions, as opposed to $\textsf{NCSym}$. For an unlabelled directed graph $D$, our extension of chromatic symmetric functions provides an element $\text{{X}}_{D}$ in $\textsf{NSym}$, in contrast to the analogue $Y_{G} \in \textsf{NCSym}$ of $X_{G}$ due to Gebhard and Sagan. Letting $G$ denote the undirected graph underlying $D$, our construction is such that the commutative image of $\text{{X}}_{D}$ is $ X_{G}$. This projection property is achieved by lifting Stanley's power sum expansion for chromatic symmetric functions, with the use of the $\Psi$-basis of $\textsf{NSym}$, so that the orderings of the entries of the indexing compositions are determined by the directed edges of $D$. We then construct generating sets for $\textsf{NSym}$ consisting of expressions of the form $\text{{X}}_{D}$, building on the work of Cho and van Willigenburg on chromatic generating sets for $\textsf{Sym}$., Comment: Submitted for publication

As a step towards the ultimate goal of a high-precision mass measurement of doubly-magic $^{100}$Sn, the mass of $^{103}$Sn was measured at the Low Energy Beam and Ion Trap (LEBIT) located at the Facility for Rare Isotope Beams (FRIB). Utilizing the time-of-flight ion cyclotron resonance (ToF-ICR) technique, a mass uncertainty of 3.7~keV was achieved, an improvement by more than an order of magnitude compared to a recent measurement performed in 2023 at the Cooler Storage Ring (CSRe) in Lanzhou. Although the LEBIT and CSRe mass measurements of $^{103}$Sn are in agreement, they diverge from the experimental mass value reported in the 2016 version of the Atomic Mass Evaluation (AME2016), which was derived from the measured $Q_{\beta^+}$ value and the mass of $^{103}$In. In AME2020, this indirectly measured $^{103}$Sn mass was classified as a `seriously irregular mass' and replaced with an extrapolated value, which aligns with the most recent measured values from CSRe and LEBIT. As such, the smoothness of the mass surface is confidently reestablished for $^{103}$Sn. Furthermore, LEBIT's mass measurement of $^{103}$Sn enabled a significant reduction in the mass uncertainties of five parent isotopes which are now dominated by uncertainties in their respective $Q$-values.

500-nm-thick $\mathrm{In}_{x}\mathrm{Ga}_{1-x}\mathrm{N}$ layers with $x=$ 0.05-0.14 are grown using plasma-assisted molecular beam epitaxy, and their properties are assessed by a comprehensive analysis involving x-ray diffraction, secondary ion mass spectrometry, and cathodoluminescence as well as photoluminescence spectroscopy. We demonstrate low degrees of strain relaxation (10% for $x=0.12$), low threading dislocation densities ($\mathrm{1\times10^{9}\,cm^{-2}}$ for $x=0.12$), uniform composition both in the growth and lateral direction, and a narrow emission band. The unique sum of excellent materials properties make these layers an attractive basis for the top-down fabrication of ternary nanowires.

The Hamiltonian Monte Carlo (HMC) algorithm is often lauded for its ability to effectively sample from high-dimensional distributions. In this paper we challenge the presumed domination of HMC for the Bayesian analysis of GLMs. By utilizing the structure of the compute graph rather than the graphical model, we reduce the time per sweep of a full-scan Gibbs sampler from $O(d^2)$ to $O(d)$, where $d$ is the number of GLM parameters. Our simple changes to the implementation of the Gibbs sampler allow us to perform Bayesian inference on high-dimensional GLMs that are practically infeasible with traditional Gibbs sampler implementations. We empirically demonstrate a substantial increase in effective sample size per time when comparing our Gibbs algorithms to state-of-the-art HMC algorithms. While Gibbs is superior in terms of dimension scaling, neither Gibbs nor HMC dominate the other: we provide numerical and theoretical evidence that HMC retains an edge in certain circumstances thanks to its advantageous condition number scaling. Interestingly, for GLMs of fixed data size, we observe that increasing dimensionality can stabilize or even decrease condition number, shedding light on the empirical advantage of our efficient Gibbs sampler.

The emergence of anomalous Hall effects (AHE) in antiferromagnets presents an intriguing phenomenon with potential spintronic device applications due to their ultrafast switching dynamics. Mn3NiN antiperovskite stands out as a promising candidate owing to its noncollinear antiferromagnetism. In this study, we report a significant AHE observed in epitaxial Mn3NiN thin films below the N\'eel temperature, comparable to those observed in typical ferromagnets. Through a combination of magnetometry, neutron diffraction and ab-initio data analyses, we propose that the substantial AHE can be attributed to the non-vanishing Berry curvature arising from the {\Gamma}4g-type spin structure, coupled with uncompensated magnetic domains featuring a weak canted moment. This coupling, due to robust antiferromagnetic domains, enables deterministic detection and switching of AHE via the application of a magnetic field.

Accurate 3D object detection in real-world environments requires a huge amount of annotated data with high quality. Acquiring such data is tedious and expensive, and often needs repeated effort when a new sensor is adopted or when the detector is deployed in a new environment. We investigate a new scenario to construct 3D object detectors: learning from the predictions of a nearby unit that is equipped with an accurate detector. For example, when a self-driving car enters a new area, it may learn from other traffic participants whose detectors have been optimized for that area. This setting is label-efficient, sensor-agnostic, and communication-efficient: nearby units only need to share the predictions with the ego agent (e.g., car). Naively using the received predictions as ground-truths to train the detector for the ego car, however, leads to inferior performance. We systematically study the problem and identify viewpoint mismatches and mislocalization (due to synchronization and GPS errors) as the main causes, which unavoidably result in false positives, false negatives, and inaccurate pseudo labels. We propose a distance-based curriculum, first learning from closer units with similar viewpoints and subsequently improving the quality of other units' predictions via self-training. We further demonstrate that an effective pseudo label refinement module can be trained with a handful of annotated data, largely reducing the data quantity necessary to train an object detector. We validate our approach on the recently released real-world collaborative driving dataset, using reference cars' predictions as pseudo labels for the ego car. Extensive experiments including several scenarios (e.g., different sensors, detectors, and domains) demonstrate the effectiveness of our approach toward label-efficient learning of 3D perception from other units' predictions.

The prevalence of vision-threatening eye diseases is a significant global burden, with many cases remaining undiagnosed or diagnosed too late for effective treatment. Large vision-language models (LVLMs) have the potential to assist in understanding anatomical information, diagnosing eye diseases, and drafting interpretations and follow-up plans, thereby reducing the burden on clinicians and improving access to eye care. However, limited benchmarks are available to assess LVLMs' performance in ophthalmology-specific applications. In this study, we introduce LMOD, a large-scale multimodal ophthalmology benchmark consisting of 21,993 instances across (1) five ophthalmic imaging modalities: optical coherence tomography, color fundus photographs, scanning laser ophthalmoscopy, lens photographs, and surgical scenes; (2) free-text, demographic, and disease biomarker information; and (3) primary ophthalmology-specific applications such as anatomical information understanding, disease diagnosis, and subgroup analysis. In addition, we benchmarked 13 state-of-the-art LVLM representatives from closed-source, open-source, and medical domains. The results demonstrate a significant performance drop for LVLMs in ophthalmology compared to other domains. Systematic error analysis further identified six major failure modes: misclassification, failure to abstain, inconsistent reasoning, hallucination, assertions without justification, and lack of domain-specific knowledge. In contrast, supervised neural networks specifically trained on these tasks as baselines demonstrated high accuracy. These findings underscore the pressing need for benchmarks in the development and validation of ophthalmology-specific LVLMs., Comment: Project Page: https://kfzyqin.github.io/lmod/

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

Dissipative time crystals are many-body open quantum systems that exhibit non-trivial time periodic motion at late times. We examine the onset and resilience of such emergent time periodicity in a few-body all-to-all interacting Lipkin-Meshkov-Glick model, where one of the constituents is locally in contact with a thermal bath. Employing both a collision model framework and a suitable time-continuous description, we show that stable time-crystalline behavior can only be exhibited when the bath acts as purely dissipative channel. We assess the role that the microscopic interactions within the system play, establishing that for the all-to-all model the introduction of temperature leads to a melting of the dissipative time crystallinity, in contrast to stable long-time periodicity which can be maintained for nearest neighbor $XXZ$ type interactions., Comment: 7 pages, 4 figures

Low Probability of Detection (LPD) communication aims to obscure the presence of radio frequency (RF) signals to evade surveillance. In the context of mobile surveillance utilizing unmanned aerial vehicles (UAVs), achieving LPD communication presents significant challenges due to the UAVs' rapid and continuous movements, which are characterized by unknown nonlinear dynamics. Therefore, accurately predicting future locations of UAVs is essential for enabling real-time LPD communication. In this paper, we introduce a novel framework termed predictive covert communication, aimed at minimizing detectability in terrestrial ad-hoc networks under multi-UAV surveillance. Our data-driven method synergistically integrates graph neural networks (GNN) with Koopman theory to model the complex interactions within a multi-UAV network and facilitating long-term predictions by linearizing the dynamics, even with limited historical data. Extensive simulation results substantiate that the predicted trajectories using our method result in at least 63%-75% lower probability of detection when compared to well-known state-of-the-art baseline approaches, showing promise in enabling low-latency covert operations in practical scenarios.

We introduce the ECG-Image-Database, a large and diverse collection of electrocardiogram (ECG) images generated from ECG time-series data, with real-world scanning, imaging, and physical artifacts. We used ECG-Image-Kit, an open-source Python toolkit, to generate realistic images of 12-lead ECG printouts from raw ECG time-series. The images include realistic distortions such as noise, wrinkles, stains, and perspective shifts, generated both digitally and physically. The toolkit was applied to 977 12-lead ECG records from the PTB-XL database and 1,000 from Emory Healthcare to create high-fidelity synthetic ECG images. These unique images were subjected to both programmatic distortions using ECG-Image-Kit and physical effects like soaking, staining, and mold growth, followed by scanning and photography under various lighting conditions to create real-world artifacts. The resulting dataset includes 35,595 software-labeled ECG images with a wide range of imaging artifacts and distortions. The dataset provides ground truth time-series data alongside the images, offering a reference for developing machine and deep learning models for ECG digitization and classification. The images vary in quality, from clear scans of clean papers to noisy photographs of degraded papers, enabling the development of more generalizable digitization algorithms. ECG-Image-Database addresses a critical need for digitizing paper-based and non-digital ECGs for computerized analysis, providing a foundation for developing robust machine and deep learning models capable of converting ECG images into time-series. The dataset aims to serve as a reference for ECG digitization and computerized annotation efforts. ECG-Image-Database was used in the PhysioNet Challenge 2024 on ECG image digitization and classification.

Dense retrieval systems are commonly used for information retrieval (IR). They rely on learning text representations through an encoder and usually require supervised modeling via labelled data which can be costly to obtain or simply unavailable. In this study, we introduce a novel unsupervised text representation learning technique via instruction-tuning the pre-trained encoder-decoder large language models (LLM) under the dual-encoder retrieval framework. We demonstrate the corpus representation can be augmented by the representations of relevant synthetic queries generated by the instruct-tuned LLM founded on the Rao-Blackwell theorem. Furthermore, we effectively align the query and corpus text representation with self-instructed-tuning. Specifically, we first prompt an open-box pre-trained LLM to follow defined instructions (i.e. question generation and keyword summarization) to generate synthetic queries. Next, we fine-tune the pre-trained LLM with defined instructions and the generated queries that passed quality check. Finally, we generate synthetic queries with the instruction-tuned LLM for each corpora and represent each corpora by weighted averaging the synthetic queries and original corpora embeddings. We evaluate our proposed method under low-resource settings on three English and one German retrieval datasets measuring NDCG@10, MRR@100, Recall@100. We significantly improve the average zero-shot retrieval performance on all metrics, increasing open-box FLAN-T5 model variations by [3.34%, 3.50%] in absolute and exceeding three competitive dense retrievers (i.e. mDPR, T-Systems, mBART-Large), with model of size at least 38% smaller, by 1.96%, 4.62%, 9.52% absolute on NDCG@10., Comment: Accepted at DCAI24 workshop@CIKM2024

Building on the insight that engineered noise, specifically, engineered dephasing can enhance the adiabaticity of controlled quantum dynamics, we investigate how a dephasing-generating coupling to an auxiliary quantum system affects quantum annealing protocols. By calculating the exact reduced system dynamics, we show how this coupling enhances the system's adiabaticity solely through a coherent mechanism - an effective energy rescaling. We show that it can lead to an annealing speedup linearly proportional to the strength of the coupling. We discuss the experimental feasibility of the protocols, and investigate the trade-off between fidelity and implementability by examining two modified versions with fewer types of required physical couplings., Comment: 12 pages, 5 figures

Quantifying fiber disarray, which is a prominent maladaptation associated with hypertrophic cardiomyopathy, remains critical to understanding the disease's complex pathophysiology. This study investigates the role of heterogeneous impairment of fiber contractility and fibrosis in the induction of disarray and their subsequent impact on cardiac pumping function. Fiber disarray is modeled via a stress-based fiber reorientation law within a multiscale finite element cardiac modeling framework called MyoFE. Using multiscale modeling capabilities, this study quantifies the distinct impacts of hypocontractility, hypercontractility and fibrosis on the development of fiber disarray and quantifies how their contributions affect the functional characteristics of the heart.

This paper presents surface acoustic wave (SAW) acoustic delay lines (ADL) for studying propagation loss mechanisms in Lithium Niobate (LN). Devices were fabricated by depositing 50 nm aluminum patterns on 600 nm X-Cut LN on amorphous silicon on silicon carbide, where longitudinally dominant SAW was targeted. Upon fabrication, higher-order thickness-based cross-sectional Lam\'e modes and Rayleigh modes were studied for their Q factors using acoustic delay lines. Utilizing bi-directional electrodes, ADL with lateral lambda values ranging from 0.4 um to 0.6 um were measured. Higher order Lame modes were found to have consistently higher Q factors than their Rayleigh mode counterpart, on the order of 1000-3000, showing high-frequency SAW devices as still viable candidates for frequency scaling without a substantial increase in loss., Comment: 4 pages, 7 figures, accepted by IEEE UFFC-JS

Triggered by the realization that AI emulators can rival the performance of traditional numerical weather prediction models running on HPC systems, there is now an increasing number of large AI models that address use cases such as forecasting, downscaling, or nowcasting. While the parallel developments in the AI literature focus on foundation models -- models that can be effectively tuned to address multiple, different use cases -- the developments on the weather and climate side largely focus on single-use cases with particular emphasis on mid-range forecasting. We close this gap by introducing Prithvi WxC, a 2.3 billion parameter foundation model developed using 160 variables from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Prithvi WxC employs an encoder-decoder-based architecture, incorporating concepts from various recent transformer models to effectively capture both regional and global dependencies in the input data. The model has been designed to accommodate large token counts to model weather phenomena in different topologies at fine resolutions. Furthermore, it is trained with a mixed objective that combines the paradigms of masked reconstruction with forecasting. We test the model on a set of challenging downstream tasks namely: Autoregressive rollout forecasting, Downscaling, Gravity wave flux parameterization, and Extreme events estimation. The pretrained model with 2.3 billion parameters, along with the associated fine-tuning workflows, has been publicly released as an open-source contribution via Hugging Face.

We report the first measurement of the azimuthal anisotropy of J$/\psi$ at forward rapidity ($1.2<|\eta|<2.2$) in Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV at the Relativistic Heavy Ion Collider. The data were collected by the PHENIX experiment in 2014 and 2016 with integrated luminosity of 14.5~nb$^{-1}$. The second Fourier coefficient ($v_2$) of the azimuthal distribution of $J/\psi$ is determined as a function of the transverse momentum ($p_T$) using the event-plane method. The measurements were performed for several selections of collision centrality: 0\%--50\%, 10\%--60\%, and 10\%-40\%. We find that in all cases the values of $v_2(p_T)$, which quantify the elliptic flow of J$/\psi$, are consistent with zero. The results are consistent with measurements at midrapidity, indicating no significant elliptic flow of the J$/\psi$ within the quark-gluon-plasma medium at collision energies of $\sqrt{s_{_{NN}}}=200$ GeV., Comment: 369 authors from 72 institutions, 12 pages, 7 figures, 5 tables. v1 is version submitted to Physical Review C. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

We present the first forward-rapidity measurements of elliptic anisotropy of open-heavy-flavor muons at the BNL Relativistic Heavy Ion Collider. The measurements are based on data samples of Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV collected by the PHENIX experiment in 2014 and 2016 with integrated luminosity of 14.5~nb$^{-1}$. The measurements are performed in the pseudorapidity range $1.2<|\eta|<2$ and cover transverse momenta $1