Search

Capacitary measures form a class of measures that vanish on sets of capacity zero. These measures are compact with respect to so-called $\gamma$-convergence, which relates a sequence of measures to the sequence of solutions of relaxed Dirichlet problems. This compactness result is already known for the classical $H^1(\Omega)$-capacity. This paper extends it to the fractional capacity defined for fractional order Sobolev spaces $H^s(\Omega)$ for $s\in (0,1)$. The compactness result is applied to obtain a finer optimality condition for a class of minimization problems in $H^s(\Omega)$., Comment: 31 pages, 11 figures

Axions are a well-motivated candidate for dark matter. The preeminent method to search for axion dark matter is known as the axion haloscope, which makes use of the conversion of axions to photons in a large magnetic field. Due to the weak coupling of axions to photons however, the expected signal strength is exceptionally small. To increase signal strength, many haloscopes make use of resonant enhancement and high gain amplifiers, while also taking measures to keep receiver noise as low as possible such as the use of dilution refrigerators and ultra low-noise electronics. In this paper we derive the theoretical noise model based on the sources of noise found within a typical axion haloscope receiver chain, using the Axion Dark Matter eXperiment (ADMX) as a case study. We present examples of different noise calibration measurements at 1280~MHz using a variable temperature stage with ADMX during its most recent data taking run. The consistency between the measurements and the detailed model provide suggestions for future improvements within ADMX and other axion haloscopes to reach a lower noise temperature and to simplify the receiver chain design.

The Axion Dark Matter eXperiment is sensitive to narrow axion flows, given axions compose a fraction of the dark matter with a non-negligible local density. Detecting these low-velocity dispersion flows requires a high spectral resolution and careful attention to the expected signal modulation due to Earth's motion. We report an exclusion on the local axion dark matter density in narrow flows of $\rho_a \gtrsim 0.03\,\mathrm{GeV/cm^3}$ and $\rho_a \gtrsim 0.004\,\mathrm{GeV/cm^3}$ for Dine-Fischler-Srednicki-Zhitnitski and Kim-Shifman-Vainshtein-Zakharov axion-photon couplings, respectively, over the mass range $3.3-4.2\,\mu\text{eV}$. Measurements were made at selected resolving powers to allow for a range of possible velocity dispersions., Comment: 7 pages, 3 figures

Twisted bilayer graphene (tBLG) near the magic angle is a unique platform where the combination of topology and strong correlations gives rise to exotic electronic phases. These phases are gate-tunable and related to the presence of flat electronic bands, isolated by single-particle band gaps. This enables gate-controlled charge confinement, essential for the operation of single-electron transistors (SETs), and allows to explore the interplay of confinement, electron interactions, band renormalisation and the moir\'e superlattice, potentially revealing key paradigms of strong correlations. Here, we present gate-defined SETs in near-magic-angle tBLG with well-tunable Coulomb blockade resonances. These SETs allow to study magnetic field-induced quantum oscillations in the density of states of the source-drain reservoirs, providing insight into gate-tunable Fermi surfaces of tBLG. Comparison with tight-binding calculations highlights the importance of displacement-field-induced band renormalisation crucial for future advanced gate-tunable quantum devices and circuits in tBLG including e.g. quantum dots and Josephson junction arrays., Comment: 25 pages, 14 figures

We report the results of a QCD axion dark matter search with discovery ability for Dine-Fischler-Srednicki-Zhitnitsky (DFSZ) axions using an axion haloscope. Sub-Kelvin noise temperatures are reached with an ultra low-noise Josephson parametric amplifier cooled by a dilution refrigerator. This work excludes (with a 90% confidence level) DFSZ axions with masses between 3.27 to 3.34 {\mu}eV, assuming a standard halo model with a local energy density of 0.45 GeV/cm${}^3$ made up 100% of axions.

In Delaware, Career and Technical Education (CTE) prepares students for life beyond high school by providing practical labor skills, workforce credentials, and early post secondary credits. Some CTE students graduate with the distinction as a concentrator, meaning they complete required coursework in a specific career pathway, such as engineering & technology, health informatics, or marketing. While graduating as a concentrator benefits students in terms of job placement and wages after high school, identification of students as concentrators varies across the country. Partnership for Public Education partnered with the Delaware Department of Education (DDOE) to understand the state's identification strategies and to see if inequities in identification exist that could prohibit students from accessing the benefits of concentrating. The study highlighted in this brief explores inequities in the state's CTE identification strategies that could potentially prohibit students from benefiting from CTE, and proposes strategies for improving equity in CTE. Modest mismeasurement in concentration status, as well as some inequities in which student subgroups become a concentrator was found. In this policy brief, the authors share details on findings and strategies for improving equity in CTE.

Sparsely-activated Mixture-of-Experts (MoE) architecture has increasingly been adopted to further scale large language models (LLMs) due to its sub-linear scaling for computation costs. However, frequent failures still pose significant challenges as training scales. The cost of even a single failure is significant, as all GPUs need to wait idle until the failure is resolved, potentially losing considerable training progress as training has to restart from checkpoints. Existing solutions for efficient fault-tolerant training either lack elasticity or rely on building resiliency into pipeline parallelism, which cannot be applied to MoE models due to the expert parallelism strategy adopted by the MoE architecture. We present Lazarus, a system for resilient and elastic training of MoE models. Lazarus adaptively allocates expert replicas to address the inherent imbalance in expert workload and speeds-up training, while a provably optimal expert placement algorithm is developed to maximize the probability of recovery upon failures. Through adaptive expert placement and a flexible token dispatcher, Lazarus can also fully utilize all available nodes after failures, leaving no GPU idle. Our evaluation shows that Lazarus outperforms existing MoE training systems by up to 5.7x under frequent node failures and 3.4x on a real spot instance trace.

As the parameter size of large language models (LLMs) continues to expand, the need for a large memory footprint and high communication bandwidth have become significant bottlenecks for the training and inference of LLMs. To mitigate these bottlenecks, various tensor compression techniques have been proposed to reduce the data size, thereby alleviating memory requirements and communication pressure. Our research found that video codecs, despite being originally designed for compressing videos, show excellent efficiency when compressing various types of tensors. We demonstrate that video codecs can be versatile and general-purpose tensor codecs while achieving the state-of-the-art compression efficiency in various tasks. We further make use of the hardware video encoding and decoding module available on GPUs to create a framework capable of both inference and training with video codecs repurposed as tensor codecs. This greatly reduces the requirement for memory capacity and communication bandwidth, enabling training and inference of large models on consumer-grade GPUs.

We present an analysis of gravitational wave (GW) predictions from five two-dimensional Core Collapse Supernova (CCSN) simulations that varied only in the Equation of State (EOS) implemented. The GW signals from these simulations are used to produce spectrograms in the absence of noise, and the emergent high-frequency feature (HFF) is found to differ quantitatively between simulations. Below 1 kHz, the HFF is well approximated by a first-order polynomial in time. The resulting slope was found to vary between 10-50% across all models. Further, using real interferometric noise we investigated the current capabilities of GW detectors to resolve these differences in HFF slope for a Galactic CCSN. We find that for distances up to 1 kpc, current detectors can resolve HFF slopes that vary by at least 30%. For further Galactic distances, current detectors are capable of distinguishing the upper and lower bounds of the HFF slope for groupings of our models that varied in EOS. With the higher sensitivity of future GW detectors, and with improved analysis of the HFF, our ability to resolve properties of the HFF will improve for all Galactic distances. This study shows the potential of using the HFF of CCSN produced GWs to provide insight into the physical processes occurring deep within CCSN during collapse, and in particular its potential to further constrain the EOS through GW detection., Comment: 22 pages, 10 figures

Recent research has proposed that advanced propulsion mechanisms such as warp drives are more physically feasible than previously thought, using positive energy sources potentially sourced by known classical physics. Motivated by this, we hypothesize that an advanced inter-planetary or interstellar civilization using warp drives at sub-luminal or super-luminal speeds will broadcast detectable emissions of their travels. These technosignatures would be of significant astronomical, physical, and technological interest. This paper seeks to motivate signatures from warp drive emissions due to intrinsic and extrinsic processes across several messenger types (electromagnetic, particle, and gravitational) and propose a research program to simulate such emissions in sufficient detail to search for their signatures through coordinated analyses across multiple observatories., Comment: 12 pages, 8 figures

This paper examines the extent to which large language models (LLMs) have developed higher-order theory of mind (ToM); the human ability to reason about multiple mental and emotional states in a recursive manner (e.g. I think that you believe that she knows). This paper builds on prior work by introducing a handwritten test suite -- Multi-Order Theory of Mind Q&A -- and using it to compare the performance of five LLMs to a newly gathered adult human benchmark. We find that GPT-4 and Flan-PaLM reach adult-level and near adult-level performance on ToM tasks overall, and that GPT-4 exceeds adult performance on 6th order inferences. Our results suggest that there is an interplay between model size and finetuning for the realisation of ToM abilities, and that the best-performing LLMs have developed a generalised capacity for ToM. Given the role that higher-order ToM plays in a wide range of cooperative and competitive human behaviours, these findings have significant implications for user-facing LLM applications.

Statistical models for predicting lung cancer have the potential to facilitate earlier diagnosis of malignancy and avoid invasive workup of benign disease. Many models have been published, but comparative studies of their utility in different clinical settings in which patients would arguably most benefit are scarce. This study retrospectively evaluated promising predictive models for lung cancer prediction in three clinical settings: lung cancer screening with low-dose computed tomography, incidentally detected pulmonary nodules, and nodules deemed suspicious enough to warrant a biopsy. We leveraged 9 cohorts (n=898, 896, 882, 219, 364, 117, 131, 115, 373) from multiple institutions to assess the area under the receiver operating characteristic curve (AUC) of validated models including logistic regressions on clinical variables and radiologist nodule characterizations, artificial intelligence on chest CTs, longitudinal imaging AI, and multi-modal approaches. We implemented each model from their published literature, re-training the models if necessary, and curated each cohort from primary data sources. We observed that model performance varied greatly across clinical use cases. No single predictive model emerged as a clear winner across all cohorts, but certain models excelled in specific clinical contexts. Single timepoint chest CT AI performed well in lung screening, but struggled to generalize to other clinical settings. Longitudinal imaging and multimodal models demonstrated comparatively promising performance on incidentally-detected nodules. However, when applied to nodules that underwent biopsy, all models underperformed. These results underscore the strengths and limitations of 8 validated predictive models and highlight promising directions towards personalized, noninvasive lung cancer diagnosis., Comment: Submitted to Radiology: AI

This paper focuses on the opportunities and the ethical and societal risks posed by advanced AI assistants. We define advanced AI assistants as artificial agents with natural language interfaces, whose function is to plan and execute sequences of actions on behalf of a user, across one or more domains, in line with the user's expectations. The paper starts by considering the technology itself, providing an overview of AI assistants, their technical foundations and potential range of applications. It then explores questions around AI value alignment, well-being, safety and malicious uses. Extending the circle of inquiry further, we next consider the relationship between advanced AI assistants and individual users in more detail, exploring topics such as manipulation and persuasion, anthropomorphism, appropriate relationships, trust and privacy. With this analysis in place, we consider the deployment of advanced assistants at a societal scale, focusing on cooperation, equity and access, misinformation, economic impact, the environment and how best to evaluate advanced AI assistants. Finally, we conclude by providing a range of recommendations for researchers, developers, policymakers and public stakeholders.

We present an approach to detecting (linear) gravitational wave memory in a Galactic core-collapse supernova using current interferometers. Gravitational wave memory is an important prediction of general relativity that has yet to be confirmed. Our approach uses a combination of Linear Prediction Filtering and Matched-Filtering. We present the results of our approach on data from core-collapse supernova simulations that span a range of progenitor mass and metallicity. We are able to detect gravitational wave memory out to 10 kpc. We also present the False Alarm Probabilities assuming an On-Source Window compatible with the presence of a neutrino detection., Comment: 8 pages, 5 figures

We report on the investigation of periodic superstructures in twisted bilayer graphene (tBLG) van-der-Waals heterostructures, where one of the graphene layers is aligned to hexagonal boron nitride (hBN). Our theoretical simulations reveal that if the ratio of the resulting two moir\'e unit cell areas is a simple fraction, the graphene/hBN moir\'e lattice acts as a staggered potential, breaking the degeneracy between tBLG AA sites. This leads to additional band gaps at energies where a subset of tBLG AA sites is fully occupied. These gaps manifest as Landau fans in magnetotransport, which we experimentally observe in an aligned tBLG/hBN heterostructure. Our study demonstrates the identification of commensurate tBLG/hBN van-der-Waals heterostructures by magnetotransport, highlights the persistence of moir\'e effects on length scales of tens of nanometers, and represents an interesting step forward in the ongoing effort to realise designed quantum materials with tailored properties., Comment: 18 pages, 13 figures

Nanoscale superconducting quantum interference devices (SQUIDs) are fabricated in-situ from a single Bi$_{0.26}$Sb$_{1.74}$Te$_{3}$ nanoribbon that is defined using selective-area growth and contacted with superconducting Nb electrodes via a shadow mask technique. We present $h/(2e)$ magnetic flux periodic interference in both, fully and non-fully proximitized nanoribbons. The pronounced oscillations are explained by interference effects of coherent transport through topological surface states surrounding the cross-section of the nanoribbon., Comment: 7 pages, 4 figures, supplementary material

The SCIP Optimization Suite provides a collection of software packages for mathematical optimization, centered around the constraint integer programming (CIP) framework SCIP. This report discusses the enhancements and extensions included in the SCIP Optimization Suite 9.0. The updates in SCIP 9.0 include improved symmetry handling, additions and improvements of nonlinear handlers and primal heuristics, a new cut generator and two new cut selection schemes, a new branching rule, a new LP interface, and several bug fixes. The SCIP Optimization Suite 9.0 also features new Rust and C++ interfaces for SCIP, new Python interface for SoPlex, along with enhancements to existing interfaces. The SCIP Optimization Suite 9.0 also includes new and improved features in the LP solver SoPlex, the presolving library PaPILO, the parallel framework UG, the decomposition framework GCG, and the SCIP extension SCIP-SDP. These additions and enhancements have resulted in an overall performance improvement of SCIP in terms of solving time, number of nodes in the branch-and-bound tree, as well as the reliability of the solver., Comment: The release report of the SCIP Optimization Suite version 9.0

ZnSe and related materials like ZnMgSe and ZnCdSe are promising II-VI host materials for optically mediated quantum information technology such as single photon sources or spin qubits. Integrating these heterostructures into photonic crystal (PC) cavities enables further improvements, for example realizing Purcell-enhanced single photon sources with increased quantum efficiency. Here we report on the successful implementation of two-dimensional (2D) PC cavities in strained ZnSe quantum wells (QW) on top of a novel AlAs supporting layer. This approach overcomes typical obstacles associated with PC membrane fabrication in strained materials, such as cracks and strain relaxation in the corresponding devices. We demonstrate the attainment of the required mechanical stability in our PC devices, complete strain retainment and effective vertical optical confinement. Structural analysis of our PC cavities reveals excellent etching anisotropy. Additionally, elemental mapping in a scanning transmission electron microscope confirms the transformation of AlAs into AlOx by post-growth wet oxidation and reveals partial oxidation of ZnMgSe at the etched sidewalls in the PC. This knowledge is utilized to tailor FDTD simulations and to extract the ZnMgSe dispersion relation with small oxygen content. Optical characterization of the PC cavities with cross-polarized resonance scattering spectroscopy verifies the presence of cavity modes. The excellent agreement between simulation and measured cavity mode energies demonstrates wide tunability of the PC cavity and proves the pertinence of our model. This implementation of 2D PC cavities in the ZnSe material system establishes a solid foundation for future developments of ZnSe quantum devices.

We investigate time-dependent optimization problems in fractional Sobolev spaces with the sparsity promoting $L^p$-pseudo norm for $0

Large language models (LLMs) have seen significant adoption for natural language tasks, owing their success to massive numbers of model parameters (e.g., 70B+); however, LLM inference incurs significant computation and memory costs. Recent approaches propose parallel decoding strategies, such as Skeleton-of-Thought (SoT), to improve performance by breaking prompts down into sub-problems that can be decoded in parallel; however, they often suffer from reduced response quality. Our key insight is that we can request additional information, specifically dependencies and difficulty, when generating the sub-problems to improve both response quality and performance. In this paper, we propose Skeleton Graph Decoding (SGD), which uses dependencies exposed between sub-problems to support information forwarding between dependent sub-problems for improved quality while exposing parallelization opportunities for decoding independent sub-problems. Additionally, we leverage difficulty estimates for each sub-problem to select an appropriately-sized model, improving performance without significantly reducing quality. Compared to standard autoregressive generation and SoT, SGD achieves a 1.69x speedup while improving quality by up to 51%.

In this paper, we investigate the intersection of large generative AI models and cloud-native computing architectures. Recent large models such as ChatGPT, while revolutionary in their capabilities, face challenges like escalating costs and demand for high-end GPUs. Drawing analogies between large-model-as-a-service (LMaaS) and cloud database-as-a-service (DBaaS), we describe an AI-native computing paradigm that harnesses the power of both cloud-native technologies (e.g., multi-tenancy and serverless computing) and advanced machine learning runtime (e.g., batched LoRA inference). These joint efforts aim to optimize costs-of-goods-sold (COGS) and improve resource accessibility. The journey of merging these two domains is just at the beginning and we hope to stimulate future research and development in this area.

The ADMX collaboration gathered data for its Run 1A axion dark matter search from January to June 2017, scanning with an axion haloscope over the frequency range 645-680 MHz (2.66-2.81 ueV in axion mass) at DFSZ sensitivity. The resulting axion search found no axion-like signals comprising all the dark matter in the form of a virialized galactic halo over the entire frequency range, implying lower bound exclusion limits at or below DFSZ coupling at the 90% confidence level. This paper presents expanded details of the axion search analysis of Run 1A, including review of relevant experimental systems, data-taking operations, preparation and interpretation of raw data, axion search methodology, candidate handling, and final axion limits., Comment: 27 pages, 19 figures, accepted for publication in PRD

With increasing attention in Majorana physics for possible quantum bit applications, a large interest has been developed to understand the properties of the interface between a $s$-type superconductor and a topological insulator. Up to this point the interface analysis was mainly focused on in situ prepared Josephson junctions, which consist of two coupled single interfaces or to ex-situ fabricated single interface devices. In our work we utilize a novel fabrication process, combining selective area growth and shadow evaporation which allows the characterization of a single in situ fabricated Nb/$\mathrm{(Bi_{0.15}Sb_{0.85})_2Te_3}$ nano interface. The resulting high interface transparency is apparent by a zero bias conductance increase by a factor of 1.7. Furthermore, we present a comprehensive differential conductance analysis of our single in situ interface for various magnetic fields, temperatures and gate voltages. Additionally, density functional theory calculations of the superconductor/topological insulator interface are performed in order to explain the peak-like shape of our differential conductance spectra and the origin of the observed smearing of conductance features., Comment: Main manuscript: 11 pages, 6 figures, Supplementary material: 10 pages, 9 figures

Shannon Entropy is the preeminent tool for measuring the level of uncertainty (and conversely, information content) in a random variable. In the field of communications, entropy can be used to express the information content of given signals (represented as time series) by considering random variables which sample from specified subsequences. In this paper, we will discuss how an entropy variant, the \textit{permutation entropy} can be used to study and classify radio frequency signals in a noisy environment. The permutation entropy is the entropy of the random variable which samples occurrences of permutation patterns from time series given a fixed window length, making it a function of the distribution of permutation patterns. Since the permutation entropy is a function of the relative order of data, it is (global) amplitude agnostic and thus allows for comparison between signals at different scales. This article is intended to describe a permutation patterns approach to a data driven problem in radio frequency communications research, and includes a primer on all non-permutation pattern specific background. An empirical analysis of the methods herein on radio frequency data is included. No prior knowledge of signals analysis is assumed, and permutation pattern specific notation will be included. This article serves as a self-contained introduction to the relationship between permutation patterns, entropy, and signals analysis for studying radio frequency signals and includes results on a classification task.

The Axion Dark Matter eXperiment (ADMX) has previously excluded Dine-Fischler-Srednicki-Zhitnisky (DFSZ) axions between 680-790 MHz under the assumption that the dark matter is described by the isothermal halo model. However, the precise nature of the velocity distribution of dark matter is still unknown, and alternative models have been proposed. We report the results of a non-virialized axion search over the mass range 2.81-3.31 {\mu}eV, corresponding to the frequency range 680-800 MHz. This analysis marks the most sensitive search for non-virialized axions sensitive to Doppler effects in the Milky Way Halo to date. Accounting for frequency shifts due to the detector's motion through the Galaxy, we exclude cold flow relic axions with a velocity dispersion of order 10^-7 c with 95% confidence.

We propose a "Moral Imagination" methodology to facilitate a culture of responsible innovation for engineering and product teams in technology companies. Our approach has been operationalized over the past two years at Google, where we have conducted over 50 workshops with teams across the organization. We argue that our approach is a crucial complement to existing formal and informal initiatives for fostering a culture of ethical awareness, deliberation, and decision-making in technology design such as company principles, ethics and privacy review procedures, and compliance controls. We characterize some of the distinctive benefits of our methodology for the technology sector in particular., Comment: 16 pages, 1 figure