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We discuss the negativity of the moment of inertia of (quark-)gluon plasma in a window of "supervortical" range of temperatures above the deconfining phase transition, $T \simeq (1\dots 1.5) T_c $ found recently in numerical Monte Carlo simulations by two independent methods. In our work, we confirm numerically that the origin of this effect is rooted in the thermal evaporation of the non-perturbative chromomagnetic condensate. We argue that the negative moment of inertia of gluon plasma indicates the presence of a novel effect, the negative spin-vortical coupling for gluons resulting in a negative gluonic Barnett effect: the spin polarization of gluons exceeds the total angular momentum of rotating plasma, thus forcing the orbital angular momentum to take negative values in the supervortical range of temperatures., Comment: 11 pages, 3 figures; acknowledgements added; discussion added; version accepted for publication in PRD

The Barnett effect, discovered more than a century ago, describes how an inertial body with otherwise zero net magnetic moment acquires spontaneous magnetization when mechanically spinning. Breakthrough experiments have recently shown that an ultrashort laser pulse destroys the magnetization of an ordered ferromagnet within hundreds of femtoseconds, with the spins losing angular momentum to circularly-polarized optical phonons as part of the ultrafast Einstein-de Haas effect. However, the prospect of using such high-frequency vibrations of the lattice to reciprocally switch magnetization in a nearby magnetic medium has not yet been experimentally explored. Here we show that the spontaneous magnetization temporarily gained via the ultrafast Barnett effect, through the resonant excitation of circularly-polarized optical phonons in paramagnetic substrates, can be used to permanently reverse the magnetic state of the substrate-mounted heterostructure. With the handedness of the phonons steering the direction of magnetic switching, the ultrafast Barnett effect offers a selective and potentially universal method for exercising ultrafast non-local control over magnetic order., Comment: Total of 31 pages and 17 figures (main text and supplementary information)

In studies on lifetimes, occasionally, the population contains statistical units that are born before the data collection has started. Left-truncated are units that deceased before this start. For all other units, the age at the study start often is recorded and we aim at testing whether this second measurement is independent of the genuine measure of interest, the lifetime. Our basic model of dependence is the one-parameter Gumbel-Barnett copula. For simplicity, the marginal distribution of the lifetime is assumed to be Exponential and for the age-at-study-start, namely the distribution of birth dates, we assume a Uniform. Also for simplicity, and to fit our application, we assume that units that die later than our study period, are also truncated. As a result from point process theory, we can approximate the truncated sample by a Poisson process and thereby derive its likelihood. Identification, consistency and asymptotic distribution of the maximum-likelihood estimator are derived. Testing for positive truncation dependence must include the hypothetical independence which coincides with the boundary of the copula's parameter space. By non-standard theory, the maximum likelihood estimator of the exponential and the copula parameter is distributed as a mixture of a two- and a one-dimensional normal distribution. The application are 55 thousand double-truncated lifetimes of German businesses that closed down over the period 2014 to 2016. The likelihood has its maximum for the copula parameter at the parameter space boundary so that the $p$-value of test is $0.5$. The life expectancy does not increase relative to the year of foundation. Using a Farlie-Gumbel-Morgenstern copula, which models positive and negative dependence, finds that life expectancy of German enterprises even decreases significantly over time., Comment: 42 pages, 17 figures

Rapidly spinning magnetic grains can acquire large magnetic dipole moments due to the Barnett effect. Here we study the new effect of Barnett magnetic dipole-dipole interaction on grain-grain collisions and grain growth, assuming grains spun up by radiative torques. We find that the collision rate between grains having embedded iron inclusions can be significantly enhanced due to Barnett magnetic dipole-dipole interaction when grains rotate suprathermally by radiative torques. We discuss the implications of enhanced collision rate for grain growth and destruction in the circumstellar envelope of evolved stars, photodissociation regions, and protostellar environments. Our results first reveal the importance of the dust magnetic properties and the local radiation field on grain growth and destruction., Comment: 12 pages, 6 figures

First, I study the analogy between the magnetization of a material and the spin polarization of particles in a fluid. Using the relativistic version of the Barnett effect, i.e. the magnetization of a material induced by mechanical rotation, the spin polarization induced by thermal vorticity is obtained within a purely classical model, where spin is treated as an intrinsic magnetic moment and rotation is included as a non-inertial effect. I argue that since spin polarization induced by thermal vorticity can be obtained in a classical theory, it can not be dominated by quantum anomalies. Second, the spin polarization induced by magnetic field is obtained for a fluid at local thermal equilibrium using statistical quantum field theory. The obtained formula is valid beyond the weak field approximation and when contributions from the non-homogeneity of the magnetic field are small. The exact form of spin polarization is studied for a free Dirac field at global equilibrium, and, like magnetic susceptibility, it oscillates according to the de Haas - van Alphen effect. Finally, I briefly review how magnetic field contributes to the difference between the spin polarization of $\Lambda$ and $\bar{\Lambda}$ observed in heavy-ion collisions., Comment: 38 pages, 4 figures; V2: new abstract, added a figure, final version

We investigate the Leggett-Garg inequalities (LGIs) for a boson system whose observables are given by deforming the Pegg-Barnett phase operators. We consider two observables and show that the quantum Fourier transform is useful in the realization of the required measurements. Deriving explicit forms for the LGIs using the coherent state $|\alpha\rangle$ as the initial state, we explore the regimes where they are violated when the time difference between observations of the phase operators is varied. We show that the system remains nonclassical in the large amplitude limit without dissipation, however with dissipation, our violation diminishes rapidly., Comment: 20 pages, 10 eps figures, latex2e; v2: minor clarifications, 10 references added, figures improved

The problem of defining a hermitian quantum phase operator is nearly as old as quantum mechanics itself. Throughout the years, a number of solutions was proposed, ranging from abstract operator formalisms to phase-space methods. In this work, we make an explicit connection between two of the most prominent approaches, by proving that the probability distribution of phase in the Paul formalism follows exactly from the Pegg-Barnett formalism by combining the latter with the quantum limited amplifier channel. Our findings suggest that the Paul framework may be viewed as a semi-classical limit of the Pegg-Barnett approach., Comment: 8 pages + 2-page Appendix

Combining the technologies of quantum optics and magnonics, we find that the circularly polarized laser can dynamically realize the quasiequilibrium magnon Bose-Einstein condensates (BEC). The Zeeman coupling between the laser and spins generates the optical Barnett field, and its direction is controllable by switching the laser chirality. We show that the optical Barnett field develops the total magnetization in insulating ferrimagnets with reversing the local magnetization, which leads to the quasiequilibrium magnon BEC. This laser-induced magnon BEC transition through optical Barnett effect, dubbed the optomagnonic Barnett effect, provides an access to coherent magnons in the high frequency regime of the order of terahertz. We also propose a realistic experimental setup to observe the optomagnonic Barnett effect using current device and measurement technologies as well as the laser chirping. The optomagnonic Barnett effect is a key ingredient for the application to ultrafast spin transport., Comment: 5+7 pages, 3 figures, 1 table

We demonstrate that the angular momentum compensation temperature $T_A$, at which the net angular momentum in the sample disappears, can be controlled in Ho$_3$Fe$_5$O$_{12}$ by partially substituting Dy for Ho. The $T_A$ can be detected using the Barnett effect, by which mechanical rotation magnetizes an object due to spin-rotation coupling. We found that $T_A$ increases with the Dy content and clarified that the $T_A$ of Ho$_{1.5}$Dy$_{1.5}$Fe$_5$O$_{12}$ coincides with room temperature. The Barnett effect enables us to explore materials applicable to magnetic devices utilizing the angular momentum compensation only by rotating the powder sample at room temperature., Comment: 5pages, 4figure

We discuss the spin, the angular momentum, and the magnetic moment of rotating chiral fermions using a kinetic theory. We find that, in addition to the chiral vortical contribution along the rotation axis, finite circular spin polarization is induced by the spin-momentum correlation of chiral fermions, which is canceled by a change in the orbital angular momentum. We point out that the eddy magnetic moment is nonvanishing due to the $g$-factors, exhibiting the chiral Barnett effect., Comment: 9 pages, 1 figure; Some typos are fixed and some reference are added

This reply is in response to commentaries by Barnett, Barrett, and Seth (arXiv:1708.08001) and Faes, Stramaglia, and Marinazzo (arXiv:1708.06990) on our paper entitled "A study of problems encountered in Granger causality analysis from a neuroscience perspective." (PNAS 114(34):7063-7072. 2017). In our paper, we analyzed several properties of Granger-Geweke causality (GGC) and discussed potential problems in neuroscience applications. We demonstrated: (i) that GGC, estimated using separate model fits, is either severely biased, particularly when the true model is known, or a high variance is introduced to overcome the bias; and (ii) that GGC does not reflect some component dynamics of the system. The commentaries by both Faes et al. and Barnett et al. point out that the computational problems of (i) are resolved by using recent computational methods. We acknowledge that these problems are indeed resolved by these methods. However, the traditional computation using separate model fits continues to be presented and applied. More fundamentally, the interpretational problems stemming from (ii) are not in anyway addressed by the improved methods because they are inherent to the definition of GGC. These properties are indeed acknowledged by both commentaries. We have no misconception of the GGC measure and do not claim that these properties are facially wrong. But we do discuss at length how these properties make it inappropriate and misleading for common types of scientific questions, how presentation of GGC results without model estimates are not decipherable, and how the absence of clear statements of questions of interest present further opportunities for misinterpretation.

Barnett relaxation, first described by Purcell in 1979, appears to play a major role in the alignment of grains with the interstellar magnetic field. In 1999, Lazarian and Draine proposed that Barnett relaxation and its relative, nuclear relaxation, can induce grains to flip. If this thermal flipping is rapid, then the dynamical effect of torques that are fixed relative to the grain body can be greatly reduced. To date, detailed studies of Barnett relaxation have been confined to grains exhibiting dynamic symmetry. In 2003, Weingartner argued that internal relaxation cannot induce flips in any grains, whether they exhibit dynamic symmetry or not. In this work, we develop approximate expressions for the dissipation rate and diffusion coefficient for Barnett relaxation. We revisit the issue of internally induced thermal flipping, finding that it cannot occur for grains with dynamic symmetry but does occur for grains lacking dynamic symmetry., Comment: MNRAS, in press

In quantum mechanics the position and momentum operators are related to each other via the Fourier transform. In the same way, here we show that the so-called Pegg-Barnett phase operator can be obtained by the application of the discrete Fourier transform to the number operator defined in a finite-dimensional Hilbert space. Furthermore, we show that the structure of the London-Susskind-Glogower phase operator, whose natural logarithm give rise the Pegg-Barnett phase operator, is contained into the Hamiltonian of circular waveguide arrays. Our results may find applications in the development of new finite-dimensional photonic systems with interesting phase-dependent properties.

We introduce FrontierMath, a benchmark of hundreds of original, exceptionally challenging mathematics problems crafted and vetted by expert mathematicians. The questions cover most major branches of modern mathematics -- from computationally intensive problems in number theory and real analysis to abstract questions in algebraic geometry and category theory. Solving a typical problem requires multiple hours of effort from a researcher in the relevant branch of mathematics, and for the upper end questions, multiple days. FrontierMath uses new, unpublished problems and automated verification to reliably evaluate models while minimizing risk of data contamination. Current state-of-the-art AI models solve under 2% of problems, revealing a vast gap between AI capabilities and the prowess of the mathematical community. As AI systems advance toward expert-level mathematical abilities, FrontierMath offers a rigorous testbed that quantifies their progress.

Generative audio models are rapidly advancing in both capabilities and public utilization -- several powerful generative audio models have readily available open weights, and some tech companies have released high quality generative audio products. Yet, while prior work has enumerated many ethical issues stemming from the data on which generative visual and textual models have been trained, we have little understanding of similar issues with generative audio datasets, including those related to bias, toxicity, and intellectual property. To bridge this gap, we conducted a literature review of hundreds of audio datasets and selected seven of the most prominent to audit in more detail. We found that these datasets are biased against women, contain toxic stereotypes about marginalized communities, and contain significant amounts of copyrighted work. To enable artists to see if they are in popular audio datasets and facilitate exploration of the contents of these datasets, we developed a web tool audio datasets exploration tool at https://audio-audit.vercel.app.

In the past utilities relied on in-field inspections to identify asset defects. Recently, utilities have started using drone-based inspections to enhance the field-inspection process. We consider a vast repository of drone images, providing a wealth of information about asset health and potential issues. However, making the collected imagery data useful for automated defect detection requires significant manual labeling effort. We propose a novel solution that combines synthetic asset defect images with manually labeled drone images. This solution has several benefits: improves performance of defect detection, reduces the number of hours spent on manual labeling, and enables the capability to generate realistic images of rare defects where not enough real-world data is available. We employ a workflow that combines 3D modeling tools such as Maya and Unreal Engine to create photorealistic 3D models and 2D renderings of defective assets and their surroundings. These synthetic images are then integrated into our training pipeline augmenting the real data. This study implements an end-to-end Artificial Intelligence solution to detect assets and asset defects from the combined imagery repository. The unique contribution of this research lies in the application of advanced computer vision models and the generation of photorealistic 3D renderings of defective assets, aiming to transform the asset inspection process. Our asset detection model has achieved an accuracy of 92 percent, we achieved a performance lift of 67 percent when introducing approximately 2,000 synthetic images of 2k resolution. In our tests, the defect detection model achieved an accuracy of 73 percent across two batches of images. Our analysis demonstrated that synthetic data can be successfully used in place of real-world manually labeled data to train defect detection model.

This paper presents and evaluates a framework for the coupling of subdomain-local projection-based reduced order models (PROMs) using the Schwarz alternating method following a domain decomposition (DD) of the spatial domain on which a given problem of interest is posed. In this approach, the solution on the full domain is obtained via an iterative process in which a sequence of subdomain-local problems are solved, with information propagating between subdomains through transmission boundary conditions (BCs). We explore several new directions involving the Schwarz alternating method aimed at maximizing the method's efficiency and flexibility, and demonstrate it on three challenging two-dimensional nonlinear hyperbolic problems: the shallow water equations, Burgers' equation, and the compressible Euler equations. We demonstrate that, for a cell-centered finite volume discretization and a non-overlapping DD, it is possible to obtain a stable and accurate coupled model utilizing Dirichlet-Dirichlet (rather than Robin-Robin or alternating Dirichlet-Neumann) transmission BCs on the subdomain boundaries. We additionally explore the impact of boundary sampling when utilizing the Schwarz alternating method to couple subdomain-local hyper-reduced PROMs. Our numerical results suggest that the proposed methodology has the potential to improve PROM accuracy by enabling the spatial localization of these models via domain decomposition, and achieve up to two orders of magnitude speedup over equivalent coupled full order model solutions and moderate speedups over analogous monolithic solutions.

I study the dynamic, general equilibrium implications of climate-change-linked transition risk on macroeconomic outcomes and asset prices. Climate-change-linked expectations of fossil fuel restrictions can produce a ``run on fossil fuels'' with accelerated production and decreasing spot prices, or a ``reverse run'' with restrained production and increased spot prices. The response depends on the expected economic consequences of the anticipated transition shock, and existing climate policies. Fossil fuel firm prices decrease in each case. I use a novel empirical measure of innovations in climate-related transition risk likelihood to show that dynamic empirical responses are consistent with a ``run on fossil fuel.''

This work introduces Text2FX, a method that leverages CLAP embeddings and differentiable digital signal processing to control audio effects, such as equalization and reverberation, using open-vocabulary natural language prompts (e.g., "make this sound in-your-face and bold"). Text2FX operates without retraining any models, relying instead on single-instance optimization within the existing embedding space. We show that CLAP encodes valuable information for controlling audio effects and propose two optimization approaches using CLAP to map text to audio effect parameters. While we demonstrate with CLAP, this approach is applicable to any shared text-audio embedding space. Similarly, while we demonstrate with equalization and reverberation, any differentiable audio effect may be controlled. We conduct a listener study with diverse text prompts and source audio to evaluate the quality and alignment of these methods with human perception., Comment: Submitted to ICASSP 2025

Retrieval Augmented Generation (RAG) is increasingly being used when building Generative AI applications. Evaluating these applications and RAG pipelines is mostly done manually, via a trial and error process. Automating evaluation of RAG pipelines requires overcoming challenges such as context misunderstanding, wrong format, incorrect specificity, and missing content. Prior works therefore focused on improving evaluation metrics as well as enhancing components within the pipeline using available question and answer datasets. However, they have not focused on 1) providing a schema for capturing different types of question-answer pairs or 2) creating a set of templates for generating question-answer pairs that can support automation of RAG pipeline evaluation. In this paper, we present a technique for generating variations in question-answer pairs to trigger failures in RAG pipelines. We validate 5 open-source RAG pipelines using 3 datasets. Our approach revealed the highest failure rates when prompts combine multiple questions: 91% for questions when spanning multiple documents and 78% for questions from a single document; indicating a need for developers to prioritise handling these combined questions. 60% failure rate was observed in academic domain dataset and 53% and 62% failure rates were observed in open-domain datasets. Our automated approach outperforms the existing state-of-the-art methods, by increasing the failure rate by 51% on average per dataset. Our work presents an automated approach for continuously monitoring the health of RAG pipelines, which can be integrated into existing CI/CD pipelines, allowing for improved quality., Comment: 11 pages, 5 figures, 9 tables

We use Hartnell's model for virus spread on a graph, also known as firefighting. For rooted trees, we propose an Unburning Algorithm, a type of greedy algorithm starting from the leaves and working back towards the root. We show that the algorithm saves at least half the vertices of the optimal solution and that this is bound is sharp. We confirm a conjecture of Hartke about integrality gaps when comparing linear and integer program solutions. For general graphs, we propose a Containment Protocol, which looks ahead two time steps to decide where to place vaccinations. We show that the protocol performs near optimally on four well-studied infinite grids. The protocol is available for any graph and we realize this flexibility by investigating an infinite pentagonal graph., Comment: 22 pages, 19 figures

In order to solve problems of practical importance, quantum computers will likely need to incorporate quantum error correction, where a logical qubit is redundantly encoded in many noisy physical qubits. The large physical-qubit overhead typically associated with error correction motivates the search for more hardware-efficient approaches. Here, using a microfabricated superconducting quantum circuit, we realize a logical qubit memory formed from the concatenation of encoded bosonic cat qubits with an outer repetition code of distance $d=5$. The bosonic cat qubits are passively protected against bit flips using a stabilizing circuit. Cat-qubit phase-flip errors are corrected by the repetition code which uses ancilla transmons for syndrome measurement. We realize a noise-biased CX gate which ensures bit-flip error suppression is maintained during error correction. We study the performance and scaling of the logical qubit memory, finding that the phase-flip correcting repetition code operates below threshold, with logical phase-flip error decreasing with code distance from $d=3$ to $d=5$. Concurrently, the logical bit-flip error is suppressed with increasing cat-qubit mean photon number. The minimum measured logical error per cycle is on average $1.75(2)\%$ for the distance-3 code sections, and $1.65(3)\%$ for the longer distance-5 code, demonstrating the effectiveness of bit-flip error suppression throughout the error correction cycle. These results, where the intrinsic error suppression of the bosonic encodings allows us to use a hardware-efficient outer error correcting code, indicate that concatenated bosonic codes are a compelling paradigm for reaching fault-tolerant quantum computation., Comment: Comments on the manuscript welcome!

We present nifty-ls, a software package for fast and accurate evaluation of the Lomb-Scargle periodogram. nifty-ls leverages the fact that Lomb-Scargle can be computed using a non-uniform FFT (NUFFT), which we evaluate with the Flatiron Institute NUFFT package (finufft). This approach achieves a many-fold speedup over the Press & Rybicki (1989) method as implemented in Astropy and is simultaneously many orders of magnitude more accurate. nifty-ls also supports fast evaluation on GPUs via CUDA and integrates with the Astropy Lomb-Scargle interface. nifty-ls is publicly available as open-source software., Comment: 4 pages, 1 figure, submitted to RNAAS. Software available at https://github.com/flatironinstitute/nifty-ls/

Diffusion-weighted imaging (DWI) is a type of Magnetic Resonance Imaging (MRI) technique sensitised to the diffusivity of water molecules, offering the capability to inspect tissue microstructures and is the only in-vivo method to reconstruct white matter fiber tracts non-invasively. The DWI signal can be analysed with the diffusion tensor imaging (DTI) model to estimate the directionality of water diffusion within voxels. Several scalar metrics, including axial diffusivity (AD), mean diffusivity (MD), radial diffusivity (RD), and fractional anisotropy (FA), can be further derived from DTI to quantitatively summarise the microstructural integrity of brain tissue. These scalar metrics have played an important role in understanding the organisation and health of brain tissue at a microscopic level in clinical studies. However, reliable DTI metrics rely on DWI acquisitions with high gradient directions, which often go beyond the commonly used clinical protocols. To enhance the utility of clinically acquired DWI and save scanning time for robust DTI analysis, this work proposes DirGeo-DTI, a deep learning-based method to estimate reliable DTI metrics even from a set of DWIs acquired with the minimum theoretical number (6) of gradient directions. DirGeo-DTI leverages directional encoding and geometric constraints to facilitate the training process. Two public DWI datasets were used for evaluation, demonstrating the effectiveness of the proposed method. Extensive experimental results show that the proposed method achieves the best performance compared to existing DTI enhancement methods and potentially reveals further clinical insights with routine clinical DWI scans., Comment: Accepted to ICONIP2024, Diffusion Weighted Imaging, Diffusion Tensor Imaging, Angular Resolution Enhancement, Fractional Anisotropy

The method of fundamental solutions (MFS) is known to be effective for solving 3D Laplace and Stokes Dirichlet boundary value problems in the exterior of a large collection of simple smooth objects. Here we present new scalable MFS formulations for the corresponding elastance and mobility problems. The elastance problem computes the potentials of conductors with given net charges, while the mobility problem -- crucial to rheology and complex fluid applications -- computes rigid body velocities given net forces and torques on the particles. The key idea is orthogonal projection of the net charge (or forces and torques) in a rectangular variant of a "completion flow". The proposal is compatible with one-body preconditioning, resulting in well-conditioned square linear systems amenable to fast multipole accelerated iterative solution, thus a cost linear in the particle number. For large suspensions with moderate lubrication forces, MFS sources on inner proxy-surfaces give accuracy on par with a well-resolved boundary integral formulation. Our several numerical tests include a suspension of 10000 nearby ellipsoids, using 26 million total preconditioned degrees of freedom, where GMRES converges to five digits of accuracy in under two hours on one workstation., Comment: 26 pages, 10 figures

International AI governance agreements and institutions may play an important role in reducing global security risks from advanced AI. To inform the design of such agreements and institutions, we conducted case studies of historical and contemporary international security agreements. We focused specifically on those arrangements around dual-use technologies, examining agreements in nuclear security, chemical weapons, biosecurity, and export controls. For each agreement, we examined four key areas: (a) purpose, (b) core powers, (c) governance structure, and (d) instances of non-compliance. From these case studies, we extracted lessons for the design of international AI agreements and governance institutions. We discuss the importance of robust verification methods, strategies for balancing power between nations, mechanisms for adapting to rapid technological change, approaches to managing trade-offs between transparency and security, incentives for participation, and effective enforcement mechanisms.

We investigate magnetization induced by mechanical rotation, known as the Barnett effect, in the two-dimensional electron gas. The energy eigenvalues of the rotating system (Barnett levels) are non-degenerate, thereby differ from Landau levels in the presence of a magnetic field. The magnetic response caused by the coupling of the Barnett gauge field to both the electron spin and orbital degree of freedom is found to be paramagnetic. Surprisingly, rotation does not cause any charge redistribution, i.e. in the two-dimensional electron gas centrifugal forces are quantum mechanically suppressed., Comment: The predicted orbital effect is equal to the Ampere magnetic moment of the rotating conduction electron density, which is canceled by the positive background charge

For over two decades, NIET has been dedicated to building educator excellence to enhance student success by supporting states, districts, schools, and educator preparation programs in recruiting, developing, and retaining educators. The first section of the 2023-24 NIET Research Summary presents highlights from five recent studies that align with specific services offered by NIET: (1) The Long Run Impacts of the TAP System on Student Outcomes in South Carolina; (2) Elevating Teacher Effectiveness for Students in Rural Arkansas; (3) Accelerating Character Education Development; (4) Building Educator Capacity in Rural Communities; and (5) NIET Aspiring Teacher Rubric: A Valid and Reliable Tool to Measure Aspiring Teacher Instruction. The subsequent section outlines findings from a comprehensive examination of NIET partnerships conducted over the past five years, illustrating the impact of NIET's services on student performance, teacher retention and effectiveness, principal development, and educator preparation.

Interpretable models are important, but what happens when the model is updated on new training data? We propose an algorithm for updating a decision tree while minimising the number of changes to the tree that a human would need to audit. We achieve this via a greedy approach that incorporates the number of changes to the tree as part of the objective function. We compare our algorithm to existing methods and show that it sits in a sweet spot between final accuracy and number of changes to audit., Comment: 12 pages

We present a limited empirical study of scaling laws for transfer learning in transformer models. More specifically, we examine a scaling law that incorporates a "transfer gap" term, indicating the effectiveness of pre-training on one distribution when optimizing for downstream performance on another distribution. When the transfer gap is low, pre-training is a cost-effective strategy for improving downstream performance. Conversely, when the gap is high, collecting high-quality fine-tuning data becomes relatively more cost effective. Fitting the scaling law to experiments from diverse datasets reveals significant variations in the transfer gap across distributions. In theory, the scaling law can inform optimal data allocation strategies and highlights how the scarcity of downstream data can bottleneck performance. Our findings contribute to a principled way to measure transfer learning efficiency and understand how data availability affects capabilities.

What techniques can be used to verify compliance with international agreements about advanced AI development? In this paper, we examine 10 verification methods that could detect two types of potential violations: unauthorized AI training (e.g., training runs above a certain FLOP threshold) and unauthorized data centers. We divide the verification methods into three categories: (a) national technical means (methods requiring minimal or no access from suspected non-compliant nations), (b) access-dependent methods (methods that require approval from the nation suspected of unauthorized activities), and (c) hardware-dependent methods (methods that require rules around advanced hardware). For each verification method, we provide a description, historical precedents, and possible evasion techniques. We conclude by offering recommendations for future work related to the verification and enforcement of international AI governance agreements.

Upcoming surveys such as Euclid, the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) and the Nancy Grace Roman Telescope (Roman) will detect hundreds of high-redshift (z > 7) quasars, but distinguishing them from the billions of other sources in these catalogues represents a significant data analysis challenge. We address this problem by extending existing selection methods by using both i) Bayesian model comparison on measured fluxes and ii) a likelihood-based goodness-of-fit test on images, which are then combined using an Fbeta statistic. The result is an automated, reproduceable and objective high-redshift quasar selection pipeline. We test this on both simulations and real data from the cross-matched Sloan Digital Sky Survey (SDSS) and UKIRT Infrared Deep Sky Survey (UKIDSS) catalogues. On this cross-matched dataset we achieve an AUC score of up to 0.795 and an F3 score of up to 0.79, sufficient to be applied to the Euclid, LSST and Roman data when available., Comment: 21 pages, 24 figures; to be submitted to the Open Journal of Astrophysics

Recent advances in machine learning have significantly improved prediction accuracy in various applications. However, ensuring the calibration of probabilistic predictions remains a significant challenge. Despite efforts to enhance model calibration, the rigorous statistical evaluation of model calibration remains less explored. In this work, we develop confidence intervals the $\ell_2$ Expected Calibration Error (ECE). We consider top-1-to-$k$ calibration, which includes both the popular notion of confidence calibration as well as full calibration. For a debiased estimator of the ECE, we show asymptotic normality, but with different convergence rates and asymptotic variances for calibrated and miscalibrated models. We develop methods to construct asymptotically valid confidence intervals for the ECE, accounting for this behavior as well as non-negativity. Our theoretical findings are supported through extensive experiments, showing that our methods produce valid confidence intervals with shorter lengths compared to those obtained by resampling-based methods.

Gaussian processes (GPs) are canonical as surrogates for computer experiments because they enjoy a degree of analytic tractability. But that breaks when the response surface is constrained, say to be monotonic. Here, we provide a mono-GP construction for a single input that is highly efficient even though the calculations are non-analytic. Key ingredients include transformation of a reference process and elliptical slice sampling. We then show how mono-GP may be deployed effectively in two ways. One is additive, extending monotonicity to more inputs; the other is as a prior on injective latent warping variables in a deep Gaussian process for (non-monotonic, multi-input) non-stationary surrogate modeling. We provide illustrative and benchmarking examples throughout, showing that our methods yield improved performance over the state-of-the-art on examples from those two classes of problems.

The heterogeneity of neurological conditions, ranging from structural anomalies to functional impairments, presents a significant challenge in medical imaging analysis tasks. Moreover, the limited availability of well-annotated datasets constrains the development of robust analysis models. Against this backdrop, this study introduces a novel approach leveraging the inherent anatomical symmetrical features of the human brain to enhance the subsequent detection and segmentation analysis for brain diseases. A novel Symmetry-Aware Cross-Attention (SACA) module is proposed to encode symmetrical features of left and right hemispheres, and a proxy task to detect symmetrical features as the Symmetry-Aware Head (SAH) is proposed, which guides the pretraining of the whole network on a vast 3D brain imaging dataset comprising both healthy and diseased brain images across various MRI and CT. Through meticulous experimentation on downstream tasks, including both classification and segmentation for brain diseases, our model demonstrates superior performance over state-of-the-art methodologies, particularly highlighting the significance of symmetry-aware learning. Our findings advocate for the effectiveness of incorporating symmetry awareness into pretraining and set a new benchmark for medical imaging analysis, promising significant strides toward accurate and efficient diagnostic processes. Code is available at https://github.com/bitMyron/sa-swin., Comment: MICCAI 2024

Basket trials test a single therapeutic treatment on several patient populations under one master protocol. A desirable adaptive design feature in these studies may be the incorporation of new baskets to an ongoing study. Limited basket sample sizes can cause issues in power and precision of treatment effect estimates which could be amplified in added baskets due to the shortened recruitment time. While various Bayesian information borrowing techniques have been introduced to tackle the issue of small sample sizes, the impact of including new baskets in the trial and into the borrowing model has yet to be investigated. We explore approaches for adding baskets to an ongoing trial under information borrowing and highlight when it is beneficial to add a basket compared to running a separate investigation for new baskets. We also propose a novel calibration approach for the decision criteria that is more robust to false decision making. Simulation studies are conducted to assess the performance of approaches which is monitored primarily through type I error control and precision of estimates. Results display a substantial improvement in power for a new basket when information borrowing is utilized, however, this comes with potential inflation of error rates which can be shown to be reduced under the proposed calibration procedure.

AI Infrastructure plays a key role in the speed and cost-competitiveness of developing and deploying advanced AI models. The current demand for powerful AI infrastructure for model training is driven by the emergence of generative AI and foundational models, where on occasion thousands of GPUs must cooperate on a single training job for the model to be trained in a reasonable time. Delivering efficient and high-performing AI training requires an end-to-end solution that combines hardware, software and holistic telemetry to cater for multiple types of AI workloads. In this report, we describe IBM's hybrid cloud infrastructure that powers our generative AI model development. This infrastructure includes (1) Vela: an AI-optimized supercomputing capability directly integrated into the IBM Cloud, delivering scalable, dynamic, multi-tenant and geographically distributed infrastructure for large-scale model training and other AI workflow steps and (2) Blue Vela: a large-scale, purpose-built, on-premises hosting environment that is optimized to support our largest and most ambitious AI model training tasks. Vela provides IBM with the dual benefit of high performance for internal use along with the flexibility to adapt to an evolving commercial landscape. Blue Vela provides us with the benefits of rapid development of our largest and most ambitious models, as well as future-proofing against the evolving model landscape in the industry. Taken together, they provide IBM with the ability to rapidly innovate in the development of both AI models and commercial offerings., Comment: Corresponding Authors: Talia Gershon, Seetharami Seelam,Brian Belgodere, Milton Bonilla

Prototypical-part models are a popular interpretable alternative to black-box deep learning models for computer vision. However, they are difficult to train, with high sensitivity to hyperparameter tuning, inhibiting their application to new datasets and our understanding of which methods truly improve their performance. To facilitate the careful study of prototypical-part networks (ProtoPNets), we create a new framework for integrating components of prototypical-part models -- ProtoPNeXt. Using ProtoPNeXt, we show that applying Bayesian hyperparameter tuning and an angular prototype similarity metric to the original ProtoPNet is sufficient to produce new state-of-the-art accuracy for prototypical-part models on CUB-200 across multiple backbones. We further deploy this framework to jointly optimize for accuracy and prototype interpretability as measured by metrics included in ProtoPNeXt. Using the same resources, this produces models with substantially superior semantics and changes in accuracy between +1.3% and -1.5%. The code and trained models will be made publicly available upon publication.

Large Language Models (LLMs) have the unique capability to understand and generate human-like text from input queries. When fine-tuned, these models show enhanced performance on domain-specific queries. OpenAI highlights the process of fine-tuning, stating: "To fine-tune a model, you are required to provide at least 10 examples. We typically see clear improvements from fine-tuning on 50 to 100 training examples, but the right number varies greatly based on the exact use case." This study extends this concept to the integration of LLMs within Retrieval-Augmented Generation (RAG) pipelines, which aim to improve accuracy and relevance by leveraging external corpus data for information retrieval. However, RAG's promise of delivering optimal responses often falls short in complex query scenarios. This study aims to specifically examine the effects of fine-tuning LLMs on their ability to extract and integrate contextual data to enhance the performance of RAG systems across multiple domains. We evaluate the impact of fine-tuning on the LLMs' capacity for data extraction and contextual understanding by comparing the accuracy and completeness of fine-tuned models against baseline performances across datasets from multiple domains. Our findings indicate that fine-tuning resulted in a decline in performance compared to the baseline models, contrary to the improvements observed in standalone LLM applications as suggested by OpenAI. This study highlights the need for vigorous investigation and validation of fine-tuned models for domain-specific tasks., Comment: 8 pages, 4 figures

We present an automatic, high-order accurate, and adaptive Brillouin zone integration algorithm for the calculation of the optical conductivity with a non-zero but small broadening factor $\eta$, focusing on the case in which a Hamiltonian in a downfolded model can be evaluated efficiently using Wannier interpolation. The algorithm uses iterated adaptive integration to exploit the localization of the transport distribution near energy and energy-difference iso-surfaces, yielding polylogarithmic computational complexity with respect to $\eta$. To demonstrate the method, we compute the AC optical conductivity of a three-band tight-binding model, and are able to resolve the Drude and interband peaks with broadening in the sub-meV regime to several digits of accuracy. Our algorithm automates convergence testing to a user-specified error tolerance, providing an important tool in black-box first-principles calculations of electrical transport phenomena and other response functions., Comment: 12 pages, 5 figures

Digital mammography is essential to breast cancer detection, and deep learning offers promising tools for faster and more accurate mammogram analysis. In radiology and other high-stakes environments, uninterpretable ("black box") deep learning models are unsuitable and there is a call in these fields to make interpretable models. Recent work in interpretable computer vision provides transparency to these formerly black boxes by utilizing prototypes for case-based explanations, achieving high accuracy in applications including mammography. However, these models struggle with precise feature localization, reasoning on large portions of an image when only a small part is relevant. This paper addresses this gap by proposing a novel multi-scale interpretable deep learning model for mammographic mass margin classification. Our contribution not only offers an interpretable model with reasoning aligned with radiologist practices, but also provides a general architecture for computer vision with user-configurable prototypes from coarse- to fine-grained prototypes., Comment: 8 pages, 6 figures, Accepted for oral presentation at the 2024 CVPR Workshop on Domain adaptation, Explainability, Fairness in AI for Medical Image Analysis (DEF-AI-MIA)

Engineering safety-critical systems such as medical devices and digital health intervention systems is complex, where long-term engagement with subject-matter experts (SMEs) is needed to capture the systems' expected behaviour. In this paper, we present a novel approach that leverages Large Language Models (LLMs), such as GPT-3.5 and GPT-4, as a potential world model to accelerate the engineering of software systems. This approach involves using LLMs to generate logic rules, which can then be reviewed and informed by SMEs before deployment. We evaluate our approach using a medical rule set, created from the pandemic intervention monitoring system in collaboration with medical professionals during COVID-19. Our experiments show that 1) LLMs have a world model that bootstraps implementation, 2) LLMs generated less number of rules compared to experts, and 3) LLMs do not have the capacity to generate thresholds for each rule. Our work shows how LLMs augment the requirements' elicitation process by providing access to a world model for domains., Comment: 5 pages, 1 figure

Does the cross-product of the position and the electromagnetic momentum density, g, include the optical spin momentum? We answer this long-standing question in the affirmative by evaluating, explicitly, the torque exerted on a particle by a beam of light carrying angular momentum.

We study the coherent state and two-mode squeezed state in the q-deformed Pegg-Barnett(PB) formalism. We show that when the truncation of the Fock space S is large enough, the phase properties of the q-deformed PB coherent state approach that of the undeformed PB coherent state. We also investigate the entanglement properties of the two-mode squeezed states in both the q-deformed and undeformed PB Fock space with the real squeezing parameter r. We see that if S is sufficiently large, the conventional two-mode squeezed states can be approximated with the PB (deformed and undeformed) states for arbitrary r. However, the value of S required increases more rapidly with the q-deformed PB states than the PB states as a function of r.

The Barnett effect refers to the magnetization induced by rotation of a demagnetized ferromagnet. We describe the location and stability of stationary states in rotating nanostructures using the Landau-Lifshitz-Gilbert equation. The conditions for an experimental observation of the Barnett effect in different materials and sample geometries are discussed., Comment: 4 pages, 2 figures

We build up a consistent theory of quantum electrodynamics in the presence of macroscopic polarizable media. We use the Huttner-Barnett model of a dispersive and absorbing dielectric medium and formulate the theory in terms of interacting quantum fields. We integrate out the damped polaritons by using diagrammatic techniques and find an exact expression for the displacement field (photon) propagator in the presence of a dispersive and absorbing dielectric half-space. This opens a new route to traceable perturbative calculations of the same kind as in free-space quantum electrodynamics. As a worked-through example we consider the interaction of a neutral atom with a dispersive and absorbing dielectric half-space. For that we use the multipolar coupling $\boldsymbol{\mu}\cdot\mathbf{D}$ of the atomic dipole moment to the electromagnetic displacement field. We apply the newly developed formalism to calculate the one-loop correction to the atomic electron propagator and find the energy-level shift and changes in the spontaneous decay rates for a neutral atom close to an absorptive dielectric mirror., Comment: 25 pages, 4 figures

Our national survey of the early intervention (EI) and early childhood special education (ECSE) workforce highlights key strengths and concerns. Workers find their roles rewarding, appreciate making a difference for children and families, and are well-qualified, with most holding at least a bachelor's degree. However, the workforce lacks diversity, which may not reflect the children served, and nearly all workers are female, while more boys receive EI and ECSE services. High-stress levels, large caseloads, inadequate support, and poor compensation are significant issues. Addressing these challenges through better training, support, and innovative solutions is essential to retain and attract qualified professionals, ensuring children and families receive the necessary services without delays. [Funding for this report was provided by Early Childhood Personnel Center at the University of Connecticut.]

The Huttner$-$Barnett model is extended to a magnetodielectric medium by adding a new matter field to this model. The eigenoperators for the coupled system are calculated and electromagnetic field is written in terms of these operators. The electric and magnetic susceptibility of the medium are explicitly derived and shown to satisfy the Kramers$-$Kronig relations. It is shown that the results obtained in this model are equivalent to the results obtained from the phenomenological methods., Comment: 25 pages

The su$(n)$ Lie algebraic structure of the Pegg-Barnett oscillator that possesses a finite-dimensional number-state space is demonstrated. The supersymmetric generalization of Pegg-Barnett oscillator is suggested. It is shown that such a supersymmetric Pegg-Barnett oscillator may have some potential applications, {\it e.g.}, the mass spectrum of the charged leptons., Comment: 4 pages, Latex, 0 figure

The oscillator algebra of Pegg-Barnett (P-B) oscillator with a finite-dimensional number-state space is investigated in this note. It is shown that the Pegg-Barnett oscillator possesses the su($n$) Lie algebraic structure. Additionally, we suggest a so-called supersymmetric P-B oscillator and discuss the related topics such as the algebraic structure and particle occupation number of supersymmetric P-B oscillator., Comment: 4 pages, Latex