Search

Social media platforms enhance the propagation of online misinformation by providing large user bases with a quick means to share content. One way to disrupt the rapid dissemination of misinformation at scale is through warning tags, which label content as potentially false or misleading. Past warning tag mitigation studies yield mixed results for diverse audiences, however. We hypothesize that personalizing warning tags to the individual characteristics of their diverse users may enhance mitigation effectiveness. To reach the goal of personalization, we need to understand how people differ and how those differences predict a person's attitudes and self-described behaviors toward tags and tagged content. In this study, we leverage Amazon Mechanical Turk (n = 132) and undergraduate students (n = 112) to provide this foundational understanding. Specifically, we find attitudes towards warning tags and self-described behaviors are positively influenced by factors such as Personality Openness and Agreeableness, Need for Cognitive Closure (NFCC), Cognitive Reflection Test (CRT) score, and Trust in Medical Scientists. Conversely, Trust in Religious Leaders, Conscientiousness, and political conservatism were negatively correlated with these attitudes and behaviors. We synthesize our results into design insights and a future research agenda for more effective and personalized misinformation warning tags and misinformation mitigation strategies more generally., Comment: 22 pages, 6 figures

This paper studies the gradient flow dynamics that arise when training deep homogeneous neural networks, starting with small initializations. The present work considers neural networks that are assumed to have locally Lipschitz gradients and an order of homogeneity strictly greater than two. This paper demonstrates that for sufficiently small initializations, during the early stages of training, the weights of the neural network remain small in norm and approximately converge in direction along the Karush-Kuhn-Tucker (KKT) points of the neural correlation function introduced in [1]. Additionally, for square loss and under a separability assumption on the weights of neural networks, a similar directional convergence of gradient flow dynamics is shown near certain saddle points of the loss function.

This paper examines gradient flow dynamics of two-homogeneous neural networks for small initializations, where all weights are initialized near the origin. For both square and logistic losses, it is shown that for sufficiently small initializations, the gradient flow dynamics spend sufficient time in the neighborhood of the origin to allow the weights of the neural network to approximately converge in direction to the Karush-Kuhn-Tucker (KKT) points of a neural correlation function that quantifies the correlation between the output of the neural network and corresponding labels in the training data set. For square loss, it has been observed that neural networks undergo saddle-to-saddle dynamics when initialized close to the origin. Motivated by this, this paper also shows a similar directional convergence among weights of small magnitude in the neighborhood of certain saddle points., Comment: tmlr-final-version

External audits of AI systems are increasingly recognized as a key mechanism for AI governance. The effectiveness of an audit, however, depends on the degree of access granted to auditors. Recent audits of state-of-the-art AI systems have primarily relied on black-box access, in which auditors can only query the system and observe its outputs. However, white-box access to the system's inner workings (e.g., weights, activations, gradients) allows an auditor to perform stronger attacks, more thoroughly interpret models, and conduct fine-tuning. Meanwhile, outside-the-box access to training and deployment information (e.g., methodology, code, documentation, data, deployment details, findings from internal evaluations) allows auditors to scrutinize the development process and design more targeted evaluations. In this paper, we examine the limitations of black-box audits and the advantages of white- and outside-the-box audits. We also discuss technical, physical, and legal safeguards for performing these audits with minimal security risks. Given that different forms of access can lead to very different levels of evaluation, we conclude that (1) transparency regarding the access and methods used by auditors is necessary to properly interpret audit results, and (2) white- and outside-the-box access allow for substantially more scrutiny than black-box access alone., Comment: FAccT 2024

Due to a result by Glasner and Downarowicz, it is known that a minimal system is mean equicontinuous if and only if it is an isomorphic extension of its maximal equicontinuous factor. The majority of known examples of this type are almost automorphic, that is, the factor map to the maximal equicontinuous factor is almost one-to-one. The only cases of isomorphic extensions which are not almost automorphic are again due to Glasner and Downarowicz, who in the same article provide a construction of such systems in a rather general topological setting. Here, we use the Anosov-Katok method in order to provide an alternative route to such examples and to show that these may be realised as smooth skew product diffeomorphisms of the two-torus with an irrational rotation on the base. Moreover - and more importantly - a modification of the construction allows to ensure that lifts of these diffeomorphism to finite covering spaces provide novel examples of finite-to-one topomorphic extensions of irrational rotations. These are still strictly ergodic and share the same dynamical eigenvalues as the original system, but show an additional singular continuous component of the dynamical spectrum.

Gamma-ray observations have established energetic isolated pulsars as outstanding particle accelerators and antimatter factories in the Galaxy. There is, however, no consensus regarding the acceleration mechanisms and the radiative processes at play, nor the locations where these take place. The spectra of all observed gamma-ray pulsars to date show strong cutoffs or a break above energies of a few gigaelectronvolt (GeV). Using the H.E.S.S. array of Cherenkov telescopes, we discovered a novel radiation component emerging beyond this generic GeV cutoff in the Vela pulsar's broadband spectrum. The extension of gamma-ray pulsation energies up to at least 20 teraelectronvolts (TeV) shows that Vela pulsar can accelerate particles to Lorentz factors higher than $4\times10^7$. This is an order of magnitude larger than in the case of the Crab pulsar, the only other pulsar detected in the TeV energy range. Our results challenge the state-of-the-art models for high-energy emission of pulsars while providing a new probe, i.e. the energetic multi-TeV component, for constraining the acceleration and emission processes in their extreme energy limit., Comment: 38 pages, 6 figures. This preprint has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this article is published in Nature Astronomy, Nat Astron (2023), and is available online at https://doi.org/10.1038/s41550-023-02052-3

Xsuite is a newly developed modular simulation package combining in a single flexible and modern framework the capabilities of different tools developed at CERN in the past decades, notably Sixtrack, Sixtracklib, COMBI and PyHEADTAIL. The suite consists of a set of Python modules (Xobjects, Xpart, Xtrack, Xcoll, Xfields, Xdeps) that can be flexibly combined together and with other accelerator-specific and general-purpose python tools to study complex simulation scenarios. The code allows for symplectic modeling of the particle dynamics, combined with the effect of synchrotron radiation, impedances, feedbacks, space charge, electron cloud, beam-beam, beamstrahlung, and electron lenses. For collimation studies, beam-matter interaction is simulated using the K2 scattering model or interfacing Xsuite with the BDSIM/Geant4 library. Tools are available to compute the accelerator optics functions from the tracking model and to generate particle distributions matched to the optics. Different computing platforms are supported, including conventional CPUs, as well as GPUs from different vendors.

The accelerated change in our planet due to human activities has led to grand societal challenges including health crises, intensified extreme weather events, food security, environmental injustice, etc. Digital twin systems combined with emerging technologies such as artificial intelligence and edge computing provide opportunities to support planning and decision-making to address these challenges. Digital twins for Earth systems (DT4ESs) are defined as the digital representation of the complex integrated Earth system including both natural processes and human activities. They have the potential to enable a diverse range of users to explore what-if scenarios across spatial and temporal scales to improve our understanding, prediction, mitigation, and adaptation to grand societal challenges. The 4th NOAA AI Workshop convened around 100 members who are developing or interested in participating in the development of DT4ES to discuss a shared community vision and path forward on fostering a future ecosystem of interoperable DT4ES. This paper summarizes the workshop discussions around DT4ES. We first defined the foundational features of a viable digital twins for Earth system that can be used to guide the development of various use cases of DT4ES. Finally, we made practical recommendations for the community on different aspects of collaboration in order to enable a future ecosystem of interoperable DT4ES, including equity-centered use case development, community-driven investigation of interoperability for DT4ES, trust-oriented co-development, and developing a community of practice., Comment: This whitepaper is an outcome of the 4th NOAA AI Workshop

A mediator observes no-regret learners playing an extensive-form game repeatedly across $T$ rounds. The mediator attempts to steer players toward some desirable predetermined equilibrium by giving (nonnegative) payments to players. We call this the steering problem. The steering problem captures problems several problems of interest, among them equilibrium selection and information design (persuasion). If the mediator's budget is unbounded, steering is trivial because the mediator can simply pay the players to play desirable actions. We study two bounds on the mediator's payments: a total budget and a per-round budget. If the mediator's total budget does not grow with $T$, we show that steering is impossible. However, we show that it is enough for the total budget to grow sublinearly with $T$, that is, for the average payment to vanish. When players' full strategies are observed at each round, we show that constant per-round budgets permit steering. In the more challenging setting where only trajectories through the game tree are observable, we show that steering is impossible with constant per-round budgets in general extensive-form games, but possible in normal-form games or if the per-round budget may itself depend on $T$. We also show how our results can be generalized to the case when the equilibrium is being computed online while steering is happening. We supplement our theoretical positive results with experiments highlighting the efficacy of steering in large games.

We introduce a new approach for computing optimal equilibria via learning in games. It applies to extensive-form settings with any number of players, including mechanism design, information design, and solution concepts such as correlated, communication, and certification equilibria. We observe that optimal equilibria are minimax equilibrium strategies of a player in an extensive-form zero-sum game. This reformulation allows to apply techniques for learning in zero-sum games, yielding the first learning dynamics that converge to optimal equilibria, not only in empirical averages, but also in iterates. We demonstrate the practical scalability and flexibility of our approach by attaining state-of-the-art performance in benchmark tabular games, and by computing an optimal mechanism for a sequential auction design problem using deep reinforcement learning.

The origin of the gamma-ray emission from M87 is currently a matter of debate. This work aims to localize the VHE (100 GeV-100 TeV) gamma-ray emission from M87 and probe a potential extended hadronic emission component in the inner Virgo Cluster. The search for a steady and extended gamma-ray signal around M87 can constrain the cosmic-ray energy density and the pressure exerted by the cosmic rays onto the intra-cluster medium, and allow us to investigate the role of the cosmic rays in the active galactic nucleus feedback as a heating mechanism in the Virgo Cluster. H.E.S.S. telescopes are sensitive to VHE gamma rays and have been utilized to observe M87 since 2004. We utilized a Bayesian block analysis to identify M87 emission states with H.E.S.S. observations from 2004 until 2021, dividing them into low, intermediate, and high states. Because of the causality argument, an extended ($\gtrsim$kpc) signal is allowed only in steady emission states. Hence, we fitted the morphology of the 120h low state data and found no significant gamma-ray extension. Therefore, we derived for the low state an upper limit of 58"(corresponding to $\approx$4.6kpc) in the extension of a single-component morphological model described by a rotationally symmetric 2D Gaussian model at 99.7% confidence level. Our results exclude the radio lobes ($\approx$30 kpc) as the principal component of the VHE gamma-ray emission from the low state of M87. The gamma-ray emission is compatible with a single emission region at the radio core of M87. These results, with the help of two multiple-component models, constrain the maximum cosmic-ray to thermal pressure ratio $X_{{CR,max.}}$$\lesssim$$0.32$ and the total energy in cosmic-ray protons (CRp) to $U_{CR}$$\lesssim$5$\times10^{58}$ erg in the inner 20kpc of the Virgo Cluster for an assumed CRp power-law distribution in momentum with spectral index $\alpha_{p}$=2.1., Comment: 15 pages, 7 figures. Accepted for publication in A&A. Corresponding authors: Victor Barbosa Martins, Stefan Ohm, Cornelia Arcaro, Natalia \.Zywucka, Mathieu de Naurois

Quantum computers require high fidelity quantum gates. These gates are obtained by routine calibration tasks that eat into the availability of cloud-based devices. Restless circuit execution speeds-up characterization and calibration by foregoing qubit reset in between circuits. Post-processing the measured data recovers the desired signal. However, since the qubits are not reset, leakage -- typically present at the beginning of the calibration -- may cause issues. Here, we develop a simulator of restless circuit execution based on a Markov Chain to study the effect of leakage. In the context of error amplifying single-qubit gates sequences, we show that restless calibration tolerates up to 0.5% of leakage which is large compared to the $10^{-4}$ gate fidelity of modern single-qubit gates. Furthermore, we show that restless circuit execution with leaky gates reduces by 33% the sensitivity of the ORBIT cost function developed by J. Kelly et al. which is typically used in closed-loop optimal control~[Phys. Rev. Lett. 112, 240504 (2014)]. Our results are obtained with standard qubit state discrimination showing that restless circuit execution is resilient against misclassified non-computational states. In summary, the restless method is sufficiently robust against leakage in both standard and closed-loop optimal control gate calibration to provided accurate results.

Geminga is an enigmatic radio-quiet gamma-ray pulsar located at a mere 250 pc distance from Earth. Extended very-high-energy gamma-ray emission around the pulsar was discovered by Milagro and later confirmed by HAWC, which are both water Cherenkov detector-based experiments. However, evidence for the Geminga pulsar wind nebula in gamma rays has long evaded detection by imaging atmospheric Cherenkov telescopes (IACTs) despite targeted observations. The detection of gamma-ray emission on angular scales > 2 deg poses a considerable challenge for the background estimation in IACT data analysis. With recent developments in understanding the complementary background estimation techniques of water Cherenkov and atmospheric Cherenkov instruments, the H.E.S.S. IACT array can now confirm the detection of highly extended gamma-ray emission around the Geminga pulsar with a radius of at least 3 deg in the energy range 0.5-40 TeV. We find no indications for statistically significant asymmetries or energy-dependent morphology. A flux normalisation of $(2.8\pm0.7)\times10^{-12}$ cm$^{-2}$s$^{-1}$TeV$^{-1}$ at 1 TeV is obtained within a 1 deg radius region around the pulsar. To investigate the particle transport within the halo of energetic leptons around the pulsar, we fitted an electron diffusion model to the data. The normalisation of the diffusion coefficient obtained of $D_0 = 7.6^{+1.5}_{-1.2} \times 10^{27}$ cm$^2$s$^{-1}$, at an electron energy of 100 TeV, is compatible with values previously reported for the pulsar halo around Geminga, which is considerably below the Galactic average., Comment: 16 pages, 15 figures, 7 tables. Accepted for publication in Astronomy & Astrophysics