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The ubiquitous noise in quantum system hinders the advancement of quantum information processing and has driven the emergence of different hardware-efficient quantum error correction protocols. Among them, qubits with structured noise, especially with biased noise, are one of the most promising platform to achieve fault-tolerance due to the high error thresholds of quantum error correction codes tailored for them. Nevertheless, their quantum operations are challenging and the demonstration of their performance beyond the fault-tolerant threshold remain incomplete. Here, we leverage Schr\"odinger cat states in a scalable planar superconducting nonlinear oscillator to thoroughly characterize the high-fidelity single-qubit quantum operations with systematic quantum tomography and benchmarking tools, demonstrating the state-of-the-art performance of operations crossing the fault-tolerant threshold of the XZZX surface code. These results thus embody a transformative milestone in the exploration of quantum systems with structured error channels. Notably, our framework is extensible to other types of structured-noise systems, paving the way for systematic characterization and validation of novel quantum platforms with structured noise., Comment: 19 pages, 12 figures

One of the most challenging problems in the computational study of localization in quantum manybody systems is to capture the effects of rare events, which requires sampling over exponentially many disorder realizations. We implement an efficient procedure on a quantum processor, leveraging quantum parallelism, to efficiently sample over all disorder realizations. We observe localization without disorder in quantum many-body dynamics in one and two dimensions: perturbations do not diffuse even though both the generator of evolution and the initial states are fully translationally invariant. The disorder strength as well as its density can be readily tuned using the initial state. Furthermore, we demonstrate the versatility of our platform by measuring Renyi entropies. Our method could also be extended to higher moments of the physical observables and disorder learning.

Collective excitations presenting nonlinear dynamics are fundamental phenomena with broad applications. A prime example is nonlinear optics, where diverse frequency mixing processes are central to communication, sensing, wavelength conversion, and attosecond physics. Leveraging recent progress in van der Waals magnetic semiconductors, we demonstrate nonlinear opto-magnonic coupling by presenting exciton states dressed by up to 20 harmonics of magnons, resulting from their nonlinearities, in the layered antiferromagnetic semiconductor CrSBr. We also create tunable optical side bands from sum- and difference-frequency generation between two optically bright magnon modes under symmetry breaking magnetic fields. Moreover, the observed difference-frequency generation mode can be continuously tuned into resonance with one of the fundamental magnons, resulting in parametric amplification of magnons. These findings realize the modulation of the optical frequency exciton with the extreme nonlinearity of magnons at microwave frequencies, which could find applications in magnonics and hybrid quantum systems, and provide new avenues for implementing opto-magnonic devices.

The widespread availability of multimodal generative models has sparked critical discussions on their fairness, reliability, and potential for misuse. While text-to-image models can produce high-fidelity, user-guided images, they also exhibit unpredictable behavior and vulnerabilities, which can be exploited to manipulate class or concept representations. To address this, we propose an evaluation framework designed to assess model reliability through their responses to globally- and locally-applied `semantic' perturbations in the embedding space, pinpointing inputs that trigger unreliable behavior. Our approach offers deeper insights into two essential aspects: (i) generative diversity, evaluating the breadth of visual representations for learned concepts, and (ii) generative fairness, examining how removing concepts from input prompts affects semantic guidance. Beyond these evaluations, our method lays the groundwork for detecting unreliable, bias-injected models and retrieval of bias provenance. We will release our code. Keywords: Fairness, Reliability, AI Ethics, Bias, Text-to-Image Models, Comment: This research is supported by the NISDRG project #20100007, funded by the Australian Government

Training multimodal generative models on large, uncurated datasets can result in users being exposed to harmful, unsafe and controversial or culturally-inappropriate outputs. While model editing has been proposed to remove or filter undesirable concepts in embedding and latent spaces, it can inadvertently damage learned manifolds, distorting concepts in close semantic proximity. We identify limitations in current model editing techniques, showing that even benign, proximal concepts may become misaligned. To address the need for safe content generation, we propose a modular, dynamic solution that leverages safety-context embeddings and a dual reconstruction process using tunable weighted summation in the latent space to generate safer images. Our method preserves global context without compromising the structural integrity of the learned manifolds. We achieve state-of-the-art results on safe image generation benchmarks, while offering controllable variation of model safety. We identify trade-offs between safety and censorship, which presents a necessary perspective in the development of ethical AI models. We will release our code. Keywords: Text-to-Image Models, Generative AI, Safety, Reliability, Model Editing, Comment: This research is supported by the NISDRG project #20100007, funded by the Australian Government

Hexagonal boron nitride (hBN) hosts quantum emitters that exhibit single-photon emission and spin-dependent fluorescence at room temperature. These features make hBN a promising platform for quantum sensing and photonics. Despite many investigations of their optical properties, the emitters' chemical structure remains unclear, as does the role of contamination at surfaces and interfaces in forming the emitters or modifying their properties. We prepare hBN samples that are compatible with both confocoal photoluminescence microscopy (PL) and transmission electron microscopy (TEM), and we use those techniques to investigate correlations between fluorescent emission, flake morphology, and surface residue. We find that the microscopy techniques themselves induce changes in hBN's optical activity and residue mophology: PL measurements induce photobleaching, whereas TEM measurements alter surface residue and emission characteristics. We also study the effects of common treatments $\unicode{x2014}$ annealing and oxygen plasma cleaning $\unicode{x2014}$ on the structure and optical activity of hBN. The results illustrate the power and importance of correlative studies to elucidate aspects of microscopic mechanisms that influence hBN's functionality as a host for quantum emitters and spin defects., Comment: 21 pages, 13 figures, 3 tables

Ultralight dark matter (ULDM), as a class of low mass (< 1 eV) dark matter (DM) candidates, is a compelling alternative to historically dominant models such as WIMPs and has recently gained significant attention in the scientific community. In this thesis, we study various experimental schemes for the direct detection of ULDM, both on ground and in space. More precisely, we propose a theoretical modeling of current and futuristic experiments, and we derive an estimation of their respective sensitivity. We mainly concentrate on three distinct phenomenologies. The first one is the coupling between a DM U(1) field, known as the dark photon (DP), and electromagnetism, which induces a small electric field oscillating at the DP Compton frequency. We study how to detect this electric field using atoms inside a cavity and through dish antennas. The second main phenomenology considered in this thesis is the oscillation of rest mass and transition frequencies of atoms and test masses. These oscillations could be produced by the non-universal coupling of standard matter with a scalar ULDM candidate (dilaton or axion-like particle). We study how to detect such couplings in classical tests of the universality of free fall (UFF), atom interferometry and using LISA. Finally, we study the effect of vacuum birefringence and dichroism induced by the coupling between axions and photons, and how it could be detected with optical cavities, fibers, and LISA., Comment: PhD thesis

Ferroelectric nematic (NF) liquid crystals combine liquid-like fluidity and orientational order of conventional nematics with macroscopic electric polarization comparable in magnitude to solid state ferroelectric materials. Here, we present a systematic study of twenty-seven homologous materials with various fluorination patterns, giving new insight into the molecular origins of spontaneous polar ordering in fluid ferroelectric nematics. Beyond our initial expectations, we find the highest stability of the NF phase to be in materials with specific fluorination patterns rather than the maximal fluorination which might be expected based on simple models. We find a delicate balance between polar and apolar nematics which is entirely dictated by the substitution of the fluorine atoms. Aided by electronic structure calculations, we show this to have its origins in the radial distribution of charge across the molecular surface, with molecules possessing a more oscillatory distribution of electrons across their surfaces possessing a higher propensity to form polar nematic phases. This work provides a new set of ground rules and designing principles which can inform the synthesis of future ferroelectric nematogens.

We consider quantum gravity with zero cosmological constant in three dimensions. First, we show that pure quantum gravity can be written as a magnetic Carrollian theory living on null infinity, described by Schwarzian-like degrees of freedom. Next, we couple quantum gravity to massless matter. Transition amplitudes exhibit several features that resemble soft graviton physics in four dimensions, despite the absence of a propagating graviton. As in four dimensions, we find three equivalent results: a soft graviton theorem, an infinite-dimensional BMS asymptotic symmetry, and a gravitational memory effect. We also resolve some extant puzzles concerning the partition function and Hilbert space of pure 3d gravity with zero cosmological constant., Comment: 36+7 pages, 2 figures

In this thesis, we present a comprehensive and pedagogical overview of dark matter (DM). Chapter 1 discusses the main evidences for its existence, its properties, and potential candidates. We then explore major detection strategies, with Chapter 2 specifically dedicated to indirect detection. In the following chapters, we study the emission of secondary photons resulting from the interaction between DM products and the Galactic environment. Chapters 3 and 4 focus on DM as sub-GeV particles, analysing how the DM-produced electrons and positrons interact with ambient photons to generate X-rays through inverse Compton scattering. Comparing the predicted spectra with data from X-ray observatories yields strong constraints on sub-GeV DM. Chapter 5 extends these techniques to the case of primordial black hole (PBH) evaporation, imposing significant limits on PBHs as potential DM candidates., Comment: 145 pages, PhD thesis

Using high-accuracy numerical relativity waveforms, we confirm the presence of numerous overtones of the $\ell=2$, $m=2$ quasinormal mode early in the ringdown of binary black hole mergers. We do this by demonstrating the stability of the mode amplitudes at different fit times, ruling out the possibility that a linear superposition of modes unphysically fits a highly nonlinear part of the waveform. We also find a number of previously unidentified subdominant second-order quasinormal modes in the $(2,2)$ mode. Even though these modes are mathematically nonlinear, they nevertheless confirm the validity of perturbation theory as a good approximation for describing much of the ringdown., Comment: 17 pages, 14 figures

Pressure injury (PI) detection is challenging, especially in dark skin tones, due to the unreliability of visual inspection. Thermography has been suggested as a viable alternative as temperature differences in the skin can indicate impending tissue damage. Although deep learning models have demonstrated considerable promise toward reliably detecting PI, the existing work fails to evaluate the performance on darker skin tones and varying data collection protocols. In this paper, we introduce a new thermal and optical imaging dataset of 35 participants focused on darker skin tones where temperature differences are induced through cooling and cupping protocols. We vary the image collection process to include different cameras, lighting, patient pose, and camera distance. We compare the performance of a small convolutional neural network (CNN) trained on either the thermal or the optical images on all skin tones. Our preliminary results suggest that thermography-based CNN is robust to data collection protocols for all skin tones., Comment: 9 pages

The transparent and conductive properties of indium tin oxide (ITO) thin films, make them an attractive coating for optically integrated ion traps. However, the relatively low transmittance for wavelengths $<$ 400 nm, high scattering and high production temperature limits the usability in trapped-ion-based quantum technologies. Here we present ITO coatings and a combined ITO + anti-reflective (AR) coating system optimized for an ion trap applied using ion beam sputtering (IBS). The coatings feature a high transmittance for wavelengths $<$ 400 nm and additional wavelengths up to 1000 nm, low scattering and low production temperature $<$ 150 $^{\circ}$C. The transmission, reflection and absorption spectra are simulated and the resistance, transmittance and scattering at 370 nm are measured for different ITO coating thicknesses and the ITO + AR coating system. For the ITO + AR coating system a resistance of 115 $\pm$ 5 $\Omega/\Box$, transmittance of 80$\%$ and scattering of 0.012 $\pm$ 0.002$\%$ at 370 nm is achieved., Comment: 13 pages, 9 figures

Effective Hamiltonian calculations for large quantum systems can be both analytically intractable and numerically expensive using standard techniques. In this manuscript, we present numerical techniques inspired by Nonperturbative Analytical Diagonalization (NPAD) and the Magnus expansion for the efficient calculation of effective Hamiltonians. While these tools are appropriate for a wide array of applications, we here demonstrate their utility for models that can be realized in circuit-QED settings. Our numerical techniques are available as an open-source Python package, ${\rm qCH_{eff}}$ (https://github.com/NVlabs/qCHeff), which uses the CuPy library for GPU-acceleration. We report up to 15x speedup on GPU over CPU for NPAD, and up to 42x speedup for the Magnus expansion (compared to QuTiP), for large system sizes., Comment: 19 pages, 8 figures. The source code is available at https://github.com/NVlabs/qCHeff

Digital media platforms (e.g., social media, science blogs) offer opportunities to communicate scientific content to general audiences at scale. However, these audiences vary in their scientific expertise, literacy levels, and personal backgrounds, making effective science communication challenging. To address this challenge, we designed TranSlider, an AI-powered tool that generates personalized translations of scientific text based on individual user profiles (e.g., hobbies, location, and education). Our tool features an interactive slider that allows users to steer the degree of personalization from 0 (weakly relatable) to 100 (strongly relatable), leveraging LLMs to generate the translations with given degrees. Through an exploratory study with 15 participants, we investigated both the utility of these AI-personalized translations and how interactive reading features influenced users' understanding and reading experiences. We found that participants who preferred higher degrees of personalization appreciated the relatable and contextual translations, while those who preferred lower degrees valued concise translations with subtle contextualization. Furthermore, participants reported the compounding effect of multiple translations on their understanding of scientific content. Given these findings, we discuss several implications of AI-personalized translation tools in facilitating communication in collaborative contexts., Comment: 23 pages, 5 figures, 1 table

AIs can beat humans in game environments; however, how helpful those agents are to human remains understudied. We augment CICERO, a natural language agent that demonstrates superhuman performance in Diplomacy, to generate both move and message advice based on player intentions. A dozen Diplomacy games with novice and experienced players, with varying advice settings, show that some of the generated advice is beneficial. It helps novices compete with experienced players and in some instances even surpass them. The mere presence of advice can be advantageous, even if players do not follow it., Comment: 9 pages, 3 figures

Global Categorical Symmetries are a powerful new tool for analyzing quantum field theories. This volume compiles lecture notes from the 2022 and 2023 summer schools on Global Categorical Symmetries, held at the Perimeter Institute for Theoretical Physics and at the Swiss Map Research Station in Les Diableret. Specifically, this volume collects the lectures: * An introduction to symmetries in quantum field theory, Kantaro Ohmori * Introduction to anomalies in quantum field theory, Clay C\'ordova * Symmetry Categories 101, Michele Del Zotto * Applied Cobordism Hypothesis, David Jordan * Finite symmetry in QFT, Daniel S. Freed These volumes are devoted to interested newcomers: we only assume (basic) knowledge of quantum field theory (QFT) and some relevant maths. We try to give appropriate references for non-standard materials that are not covered. Our aim in this first volume is to illustrate some of the main questions and ideas together with some of the methods and the techniques necessary to begin exploring global categorical symmetries of QFTs., Comment: Lecture notes from the Summer Schools of the Simons Collaboration on Global Categorical Symmetries

Instrumental variables (IVs) are widely used to estimate causal effects in the presence of unobserved confounding between exposure and outcome. An IV must affect the outcome exclusively through the exposure and be unconfounded with the outcome. We present a framework for relaxing either or both of these strong assumptions with tuneable and interpretable budget constraints. Our algorithm returns a feasible set of causal effects that can be identified exactly given relevant covariance parameters. The feasible set may be disconnected but is a finite union of convex subsets. We discuss conditions under which this set is sharp, i.e., contains all and only effects consistent with the background assumptions and the joint distribution of observable variables. Our method applies to a wide class of semiparametric models, and we demonstrate how its ability to select specific subsets of instruments confers an advantage over convex relaxations in both linear and nonlinear settings. We also adapt our algorithm to form confidence sets that are asymptotically valid under a common statistical assumption from the Mendelian randomization literature., Comment: 11 pages, 4 figures

As urbanization and climate change progress, urban heat becomes a priority for climate adaptation efforts. High temperatures concentrated in urban heat can drive increased risk of heat-related death and illness as well as increased energy demand for cooling. However, estimating the effects of urban heat is an ongoing field of research typically burdened by an imprecise description of the built environment, significant computational cost, and a lack of high-resolution estimates of the impacts of climate change. Here, we present open-source, computationally efficient machine learning methods that can improve the accuracy of urban temperature estimates when compared to historical reanalysis data. These models are applied to residential buildings in Los Angeles, and we compare the energy benefits of heat mitigation strategies to the impacts of climate change. We find that cooling demand is likely to increase substantially through midcentury, but engineered high-albedo surfaces could lessen this increase by more than 50%. The corresponding increase in heating demand complicates this narrative, but total annual energy use from combined heating and cooling with electric heat pumps in the Los Angeles urban climate is shown to benefit from the engineered cooling strategies under both current and future climates.

We assess the prospects for detecting gravitational wave echoes arising due to the quantum nature of black hole horizons with LISA. In a recent proposal, Bekenstein's black hole area quantization is connected to a discrete absorption spectrum for black holes in the context of gravitational radiation. Consequently, for incoming radiation at the black hole horizon, not all frequencies are absorbed, raising the possibility that the unabsorbed radiation is reflected, producing an echo-like signal closely following the binary coalescence waveform. In this work, we further develop this proposal by introducing a robust, phenomenologically motivated model for black hole reflectivity. Using this model, we calculate the resulting echoes for an ensemble of Numerical Relativity waveforms and examine their detectability with the LISA space-based interferometer. Our analysis demonstrates promising detection prospects and shows that, upon detection, LISA provides a direct probe of the Bekenstein-Hawking entropy. In addition, we find that the information extractable from LISA data offers valuable constraints on a wide range of quantum gravity theories., Comment: 9 pages, 8 Figures

We propose novel algorithms for generating cofaces in the Vietoris-Rips complex. Cofaces -- simplices that contain a given simplex -- have multiple important uses in generating and using a Vietoris-Rips filtered complex: both in creating the coboundary matrix for computing cohomology, and as a more recent approach for generating the simplex stream in the first place. Traditionally, most methods have generated simplices first, and then sorted them in filtration order after the generation step. In this paper, we propose generating simplex streams by generating non-expanding cofaces, which by construction produces simplices in filtration order, and we propose generating additional cofaces in filtration order using sorted neighborhood lists to produce coboundaries directly in filtration order.

We use a variational formulation to define a generalized notion of perimeter, from which we derive abstract isoperimetric Cheeger's inequalities via gradient estimates on solutions of Poisson equations. Our abstract framework unifies many existing results and in particular allows us to recover the $W_1-W^{1,1}$ transport inequality, which strengthens the usual transport-information inequality. Conversely, we also prove that Cheeger's inequality implies certain first order Calder{\'o}n-Zygmund-type gradient estimates.

Galaxy mergers are a key driver of galaxy formation and evolution, including the triggering of AGN and star formation to a still unknown degree. We thus investigate the impact of galaxy mergers on star formation and AGN activity using a sample of 3,330 galaxies at $z = [4.5, 8.5]$ from eight JWST fields (CEERS, JADES GOODS-S, NEP-TDF, NGDEEP, GLASS, El-Gordo, SMACS-0723, and MACS-0416), collectively covering an unmasked area of 189 arcmin$^2$. We focuses on star formation rate (SFR) enhancement, AGN fraction, and AGN excess in major merger ($\mu > 1/4$) close-pair samples, defined by $\Delta z < 0.3$ and projected separations $r_p < 100$ kpc, compared to non-merger samples. We find that SFR enhancement occurs only at $r_p < 20$ kpc, with values of $0.25 \pm 0.10$ dex and $0.26 \pm 0.11$ dex above the non-merger medians for $z = [4.5, 6.5]$ and $z = [6.5, 8.5]$, respectively. No other statistically significant enhancements in galaxy sSFR or stellar mass are observed at any projected separation or redshift bin. We also compare our observational results with predictions from the SC-SAM simulation and find no evidence of star formation enhancement in the simulations at any separation range. Finally, we examine the AGN fraction and AGN excess, finding that the fraction of AGNs in AGN-galaxy pairs, relative to the total AGN population, is $3.25^{+1.50}_{-1.06}$ times greater than the fraction of galaxy pairs relative to the overall galaxy population at the same redshift. We find that nearly all AGNs have a companion within 100 kpc and observe an excess AGN fraction in close-pair samples compared to non-merger samples. This excess is found to be $1.26 \pm 0.06$ and $1.34 \pm 0.06$ for AGNs identified via the inferred BPT diagram and photometric SED selection, respectively., Comment: 17 Pages, 7 Figures, Submitted to MNRAS

The West African Economic and Monetary Union (WAEMU) states, characterized by widespread smartphone usage, have witnessed banks and financial institutions introducing mobile banking applications (MBAs). These apps empower users to perform transactions such as money transfers, bill payments, and account inquiries anytime, anywhere. However, this proliferation of MBAs also raises significant security concerns. Poorly implemented security measures during app development can expose users and financial institutions to substantial financial risks through increased vulnerability to cyberattacks. Our study evaluated fifty-nine WAEMU MBAs using static analysis techniques. These MBAs were collected from the 160 banks and financial institutions of the eight WAEMU countries listed on the Central Bank of West African States (BCEAO) website. We identified security-related code issues that could be exploited by malicious actors. We investigated the issues found in the older versions to track their evolution across updates. Additionally, we identified some banks from regions such as Europe, the United States, and other developing countries and analyzed their mobile apps for a security comparison with WAEMU MBAs. Key findings include: (1) WAEMU apps exhibit security issues introduced during development, posing significant risks of exploitation; (2) Despite frequent updates, underlying security issues often persist; (3) Compared to MBAs from developed and developing countries, WAEMU apps exhibit fewer critical security issues; and (4) Apps from banks that are branches of other non-WAEMU banks often inherit security concerns from their parent apps while also introducing additional issues unique to their context. Our research underscores the need for robust security practices in WAEMU MBAs development to enhance user safety and trust in financial services., Comment: 20 pages, 11 figures, accepted at the Cybersecurity4D 2024 conference (https://2024.paicta.co)

The Minimum Volume Covering Ellipsoid (MVCE) problem, characterised by $n$ observations in $d$ dimensions where $n \gg d$, can be computationally very expensive in the big data regime. We apply methods from randomised numerical linear algebra to develop a data-driven leverage score sampling algorithm for solving MVCE, and establish theoretical error bounds and a convergence guarantee. Assuming the leverage scores follow a power law decay, we show that the computational complexity of computing the approximation for MVCE is reduced from $\mathcal{O}(nd^2)$ to $\mathcal{O}(nd + \text{poly}(d))$, which is a significant improvement in big data problems. Numerical experiments demonstrate the efficacy of our new algorithm, showing that it substantially reduces computation time and yields near-optimal solutions., Comment: 20 pages, 3 figures

We develop a hybrid GNN-CNN architecture for the reconstruction of 3-dimensional continuous cosmological matter fields from discrete point clouds, provided by observed galaxy catalogs. Using the CAMELS hydrodynamical cosmological simulations we demonstrate that the proposed architecture allows for an accurate reconstruction of both the dark matter and electron density given observed galaxies and their features. Our approach includes a learned grid assignment scheme that improves over the traditional cloud-in-cell method. Our method can improve cosmological analyses in situations where non-luminous (and thus unobservable) continuous fields need to be estimated from luminous (observable) discrete point cloud tracers., Comment: 5+5 pages, 6 figures. Contribution to ml4ps workshop at NeurIPS-2024

In a study of 107 male Y Combinator founders, a surprising correlation between age-normalized testosterone and company stage was uncovered. Testosterone, a hormone associated with confidence, dominance, and drive, increased by 55.7% from pre-seed to seed funding, peaking at the Series B stage, where levels were 99.6% higher than pre-seed. After series B funding, testosterone was observed to drop by 42.2%, coinciding with a spike in cortisol levels. This age-normalized biomarker analysis supports the dual-hormone hypothesis, illustrating that early startup success fosters feelings of dominance and confidence, while later-stage pressures and stresses erode these feelings. An alternative interpretation of the data, which suggests the opportunity for a longitudinal study, is that male founders with higher testosterone are more likely to raise larger rounds of funding., Comment: 9 pages, 4 figures

The increasing interest in autonomous driving systems has highlighted the need for an in-depth analysis of human driving behavior in diverse scenarios. Analyzing human data is crucial for developing autonomous systems that replicate safe driving practices and ensure seamless integration into human-dominated environments. This paper presents a comparative evaluation of human compliance with traffic and safety rules across multiple trajectory prediction datasets, including Argoverse 2, nuPlan, Lyft, and DeepUrban. By defining and leveraging existing safety and behavior-related metrics, such as time to collision, adherence to speed limits, and interactions with other traffic participants, we aim to provide a comprehensive understanding of each datasets strengths and limitations. Our analysis focuses on the distribution of data samples, identifying noise, outliers, and undesirable behaviors exhibited by human drivers in both the training and validation sets. The results underscore the need for applying robust filtering techniques to certain datasets due to high levels of noise and the presence of such undesirable behaviors., Comment: 8 pages, CoRL 2024 Workshop SAFE-ROL

In molecular systems, the ultrafast motion of electrons initiates the process of chemical change. Tracking this electronic motion across molecules requires coupling attosecond time resolution to atomic-scale spatial sensitivity. In this work, we employ a pair of attosecond x-ray pulses from an x-ray free-electron laser to follow electron motion resulting from the sudden removal of an electron from a prototypical aromatic system, para-aminophenol. X-ray absorption enables tracking this motion with atomic-site specificity. Our measurements are compared with state-of-the-art computational modeling, reproducing the observed response across multiple timescales. Sub-femtosecond dynamics are assigned to states undergoing non-radiative decay, while few-femtosecond oscillatory motion is associated with electronic wavepacket motion in stable cation states, that will eventually couple to nuclear motion. Our work provides insight on the ultrafast charge motion preceding and initiating chemical transformations in moderately complex systems, and provides a powerful benchmark for computational models of ultrafast charge motion in matter.

It is becoming increasingly difficult to improve the performance of a a single process (thread) on a computer due to physical limitations. Modern systems use multi-core processors in which multiple processes (threads) may run concurrently. A lock-free data structure can allow these processes to communicate with each other without requiring mutual exclusion, and may increase the amount of work they may perform in parallel rather than sequentially, thus improving the performance of the system as a whole. This paper contains an implementation of Ko's Lock-Free Binary Trie, which stores a dynamic set of keys from an ordered universe. It supports insert, remove, search and predecessor operations. One novel component of this implementation is a lock-free linked list which allows multiple processes to attempt to insert the same node, but which prevents a node from being reinserted once it has been removed from the list. The final section of this paper contains an experimental comparison of this implementation against other data structures which implement the same abstract data type (ADT) as the lock-free trie. Analysis of these experiments reveal that the implementation of Ko's Trie performs better than existing theoretical implementations of this ADT when the universe of keys is large, when removes are rare and when the number of processes performing operations concurrently is low., Comment: 6 Chapters, 71 Pages

We present differentially private algorithms for high-dimensional mean estimation. Previous private estimators on distributions over $\mathbb{R}^d$ suffer from a curse of dimensionality, as they require $\Omega(d^{1/2})$ samples to achieve non-trivial error, even in cases where $O(1)$ samples suffice without privacy. This rate is unavoidable when the distribution is isotropic, namely, when the covariance is a multiple of the identity matrix, or when accuracy is measured with respect to the affine-invariant Mahalanobis distance. Yet, real-world data is often highly anisotropic, with signals concentrated on a small number of principal components. We develop estimators that are appropriate for such signals$\unicode{x2013}$our estimators are $(\varepsilon,\delta)$-differentially private and have sample complexity that is dimension-independent for anisotropic subgaussian distributions. Given $n$ samples from a distribution with known covariance-proxy $\Sigma$ and unknown mean $\mu$, we present an estimator $\hat{\mu}$ that achieves error $\|\hat{\mu}-\mu\|_2\leq \alpha$, as long as $n\gtrsim\mathrm{tr}(\Sigma)/\alpha^2+ \mathrm{tr}(\Sigma^{1/2})/(\alpha\varepsilon)$. In particular, when $\pmb{\sigma}^2=(\sigma_1^2, \ldots, \sigma_d^2)$ are the singular values of $\Sigma$, we have $\mathrm{tr}(\Sigma)=\|\pmb{\sigma}\|_2^2$ and $\mathrm{tr}(\Sigma^{1/2})=\|\pmb{\sigma}\|_1$, and hence our bound avoids dimension-dependence when the signal is concentrated in a few principal components. We show that this is the optimal sample complexity for this task up to logarithmic factors. Moreover, for the case of unknown covariance, we present an algorithm whose sample complexity has improved dependence on the dimension, from $d^{1/2}$ to $d^{1/4}$.

We introduce PatternBoost, a flexible method for finding interesting constructions in mathematics. Our algorithm alternates between two phases. In the first ``local'' phase, a classical search algorithm is used to produce many desirable constructions. In the second ``global'' phase, a transformer neural network is trained on the best such constructions. Samples from the trained transformer are then used as seeds for the first phase, and the process is repeated. We give a detailed introduction to this technique, and discuss the results of its application to several problems in extremal combinatorics. The performance of PatternBoost varies across different problems, but there are many situations where its performance is quite impressive. Using our technique, we find the best known solutions to several long-standing problems, including the construction of a counterexample to a conjecture that had remained open for 30 years., Comment: 32 pages

Active metamaterials are engineered structures that possess novel properties that can be changed after the point of manufacture. Their novel properties arise predominantly from their physical structure, as opposed to their chemical composition and can be changed through means such as direct energy addition into wave paths, or physically changing/morphing the structure in response to both a user or environmental input. Active metamaterials are currently of wide interest to the physics community and encompass a range of sub-domains in applied physics (e.g. photonic, microwave, acoustic, mechanical, etc.). They possess the potential to provide solutions that are more suitable to specific applications, or which allow novel properties to be produced which cannot be achieved with passive metamaterials, such as time-varying or gain enhancement effects. They have the potential to help solve some of the important current and future problems faced by the advancement of modern society, such as achieving net-zero, sustainability, healthcare and equality goals. Despite their huge potential, the added complexity of their design and operation, compared to passive metamaterials creates challenges to the advancement of the field, particularly beyond theoretical and lab-based experiments. This roadmap brings together experts in all types of active metamaterials and across a wide range of areas of applied physics. The objective is to provide an overview of the current state of the art and the associated current/future challenges, with the hope that the required advances identified create a roadmap for the future advancement and application of this field.

The cosmological 21-cm signal is sourced from hyperfine transitions in neutral hydrogen atoms. Yet, although hydrogen atoms belong to the sub-class of baryonic matter, it is customary to simplify their treatment and analyze the 21-cm signal as if all the matter in the Universe was in the form of collisionless cold dark matter (CDM). This is usually done by evolving the density field via a scale-independent growth factor (SIGF). In this work, we separate the baryons from CDM and evolve the two species with a proper scale-dependent growth factor (SDGF). By incorporating the SDGF in the 21cmFirstCLASS code, we demonstrate the effect that baryons and CDM have on the 21-cm signal at the linear dark ages epoch and the subsequent non-linear epochs of cosmic dawn and reionization. Our analysis shows that the baryonic nature of hydrogen cannot be ignored during the dark ages, and that non-linear effects in density-field evolution must be accounted for after stars have formed. Furthermore, we discuss how the 21-cm signal is modified at lower redshifts, where ground-based 21-cm interferometers are mostly sensitive, due to the choice of working with either the "linear" or "non-linear" matter over-density (that is, the over-density as computed from linear perturbation theory, and non-linear perturbation theory, respectively) in the extended Press-Schechter formalism. Our code is publicly available at https://github.com/jordanflitter/21cmFirstCLASS., Comment: 30 pages, 15 figures

Biphoton frequency combs are promising resources for quantum networking due in large part to their compatibility with the telecommunication infrastructure. In this work, we propose a method to periodically compress broadband frequency-entangled photons into biphoton frequency combs by utilizing time-varying linear cavities. Our approach hinges on rapid modulation of the input cavity coupling, yielding high spectral purity in each output comb line similar to that achieved with narrowband filters, but without the associated loss in flux. We examine the dependence of spectral purity and compression on coupling strength, cavity loss, and switching speed, finding realistic regimes supporting purities in excess of 0.999 and peak enhancement factors of 100 and beyond., Comment: 5 pages, 4 figures

We present Public Domain 12M (PD12M), a dataset of 12.4 million high-quality public domain and CC0-licensed images with synthetic captions, designed for training text-to-image models. PD12M is the largest public domain image-text dataset to date, with sufficient size to train foundation models while minimizing copyright concerns. Through the Source.Plus platform, we also introduce novel, community-driven dataset governance mechanisms that reduce harm and support reproducibility over time., Comment: Project Page: https://source.plus/pd12m

Single photon detectors (SPDs) are essential technology in quantum science, quantum network, biology, and advanced imaging. To detect the small quantum of energy carried in a photon, conventional SPDs rely on energy excitation across either a semiconductor bandgap or superconducting gap. While the energy gap suppresses the false-positive error, it also sets an energy scale that can limit the detection efficiency of lower energy photons and spectral bandwidth of the SPD. Here, we demonstrate an orthogonal approach to detect single near-infrared photons using graphene calorimeters. By exploiting the extremely low heat capacity of the pseudo-relativistic electrons in graphene near its charge neutrality point, we observe an electron temperature rise up to ~2 K using a hybrid Josephson junction. In this proof-of-principle experiment, we achieve an intrinsic quantum efficiency of 87% (75%) with dark count < 1 per second (per hour) at operation temperatures as high as 1.2 K. Our results highlight the potential of electron calorimetric SPDs for detecting lower-energy photons from the mid-IR to microwave regimes, opening pathways to study space science in far-infrared regime, to search for dark matter axions, and to advance quantum technologies across a broader electromagnetic spectrum.

Early dark energy (EDE) is one of the leading models proposed to resolve the perplexing Hubble tension. Despite extensive scrutiny and testing against various observables, conclusive constraints remain elusive as we await new data. In this paper, we study the impact of EDE on the 21cm signal, a powerful probe of cosmic dawn, and the epoch of reionization. First, we examine the signatures of the shift in cosmological parameters and the new EDE parameters on the evolution of the 21cm signal compared to $\Lambda$CDM. We then focus on the implications of these signatures for upcoming radio interferometer telescopes, such as the Hydrogen Epoch of Reionization Array (HERA), and their ability to differentiate between EDE and $\Lambda$CDM. Finally, we forecast HERA's sensitivity to the fractional energy density of EDE, $f_{\rm EDE}$, assuming a fiducial EDE model. We find significant modifications to the 21cm signal due to the presence of EDE. Furthermore, our analysis suggests that HERA, operating in its designed configuration, is poised to differentiate between the models and be sensitive to $f_{\rm EDE}$ within $2\sigma$ after $\mathcal{O}(100)$ days of observation and $5\sigma$ after 2 years., Comment: 14 pages, 10 figures

The promise of large-scale, high-resolution datasets from Electron Microscopy (EM) and X-ray Microtomography (XRM) lies in their ability to reveal neural structures and synaptic connectivity, which is critical for understanding the brain. Effectively managing these complex and rapidly increasing datasets will enable new scientific insights, facilitate querying, and support secondary use across the neuroscience community. However, without effective neurodata standards that permit use of these data across multiple systems and workflows, these valuable and costly datasets risk being underutilized especially as they surpass petascale levels. These standards will promote data sharing through accessible interfaces, allow researchers to build on each other's work, and guide the development of tools and capabilities that are interoperable. Herein we outline a standards framework for creating and managing annotations originating and derived from high-resolution volumetric imaging and connectomic datasets, focusing on ensuring Findable, Accessible, Interoperable, and Reusable (FAIR) practices. The goal is to enhance collaborative efforts, boost the reliability of findings, and enable comparative analysis across growing datasets of different species and modalities. We have formed a global working group with academic and industry partners in the high-resolution volumetric data generation and analysis community, focused on identifying gaps in current EM and XRM data pipelines, and refining outlines and platforms for standardizing EM and XRM methods. This focus considers existing and past community approaches and includes examining neuronal entities, biological components, and associated metadata, while emphasizing adaptability and fostering collaboration., Comment: 11 pages, 4 figures, 1 table

We adapt Stein's method to isoperimetric and geometric inequalities. The main challenge is the treatment of boundary terms. We address this by using an elliptic PDE with an oblique boundary condition. We apply our geometric formulation of Stein's method to obtain stability of the Brock-Weinstock inequality, stability of the isoperimetric inequality under a constraint on Steklov's first non-zero eigenvalue, and stability for the combination of weighted and unweighted perimeters. All stability results are formulated with respect to the $\alpha$-Zolotarev distance, $\alpha$ $\in$ (0, 1], that we introduce to interpolate between the Fraenkel asymmetry and the Kantorovich distance.

The timeline of cosmic reionization remains uncertain despite sustained efforts to study how the ionizing output of early galaxies shaped the intergalactic medium (IGM). Using the semi-numerical code LIMFAST, we investigate the prospects for timing the reionization process by cross-correlating the 21 cm signal with the cosmic near-infrared background (NIRB) contributed by galaxies at $z>5$. Tracing opposite phases of the IGM on large scales during reionization, the two signals together serve as a powerful probe for the reionization history. However, because long-wavelength, line-of-sight Fourier modes -- the only modes probed by NIRB fluctuations -- are contaminated by 21 cm foregrounds and thus inevitably lost to foreground cleaning or avoidance, a direct cross-correlation of the two signals vanishes. We show that this problem can be circumvented by squaring the foreground-filtered 21 cm signal and cross-correlating the squared field with the NIRB. This statistic is related to the 21 cm--21 cm--NIRB cross-bispectrum and encodes valuable information regarding the reionization timeline. Particularly, the 21 cm$^2$ and NIRB signals are positively correlated during the early phases of reionization and negatively correlated at later stages. We demonstrate that this behavior is generic across several different reionization models and compare our simulated results with perturbative calculations. We show that this cross-correlation can be detected at high significance by forthcoming 21 cm and NIRB surveys such as SKA and SPHEREx. Our methodology is more broadly applicable to cross-correlations between line intensity mapping data and 2D tracers of the large-scale structure, including photometric galaxy surveys and CMB lensing mass maps, among others., Comment: 20 pages, 9 figures, submitted to AAS journals

The observed prevalence of galaxies exhibiting bursty star formation histories (SFHs) at $z\gtrsim6$ has created new challenges and opportunities for understanding their formation pathways. The degenerate effects of the efficiency and burstiness of star formation on the observed UV luminosity function are separable by galaxy clustering. However, quantifying the timescales of burstiness requires more than just the continuum UV measurements. Here we develop a flexible semi-analytic framework for modeling both the amplitude of star formation rate (SFR) variations and their temporal correlation, from which the luminosity function and clustering can be derived for SFR indicators tracing different characteristic timescales (e.g., UV continuum and H$\alpha$ luminosities). Based on this framework, we study the prospect of using galaxy summary statistics to distinguish models where SFR fluctuations are prescribed by different power spectral density (PSD) forms. Using the Fisher matrix approach, we forecast the constraints on parameters in our PSD-based model that can be extracted from mock JWST observations of the UV and H$\alpha$ luminosity functions and clustering bias factors at $z\sim6$. These constraints demonstrate the feasibility of constraining the burstiness of high-$z$ galaxies solely from their one-point and two-point statistics and underscore the importance of combining tracers of both long-term and short-term SFR variations. Our flexible framework can be readily extended to characterize the SFH of high-redshift galaxies with a wider range of observational diagnostics., Comment: 21 pages, 7 figures, submitted to JCAP

Prime-Cam, one of the primary instruments for the Fred Young Submillimeter Telescope (FYST) developed by the CCAT Collaboration, will house up to seven instrument modules, with the first operating at 280 GHz. Each module will include three arrays of superconducting microwave kinetic inductance detectors (KIDs). The first KID array fabricated for the 280 GHz module uses titanium-nitride (TiN) as the superconducting material and has 3,456 individual detectors, while the other two arrays use aluminum. This paper presents the design and laboratory characterization of the 280 GHz TiN array, which is cooled below its critical temperature to ~0.1 K and read out over six RF feedlines. LED mapping, a technique for matching the measured resonant frequency of a detector to its physical position, was performed on the array so that the results can be used to lithographically trim the KID capacitors and increase the yield of the array by reducing frequency collisions. We present the methods and results of LED mapping the 280 GHz TiN KID array before deployment on FYST., Comment: 4 pages, 4 figures, submitted to IEEE Transactions on Applied Superconductivity (IEEE TAS)

Variational inequalities (VIs) are a broad class of optimization problems encompassing machine learning problems ranging from standard convex minimization to more complex scenarios like min-max optimization and computing the equilibria of multi-player games. In convex optimization, strong convexity allows for fast statistical learning rates requiring only $\Theta(1/\epsilon)$ stochastic first-order oracle calls to find an $\epsilon$-optimal solution, rather than the standard $\Theta(1/\epsilon^2)$ calls. In this paper, we explain how one can similarly obtain fast $\Theta(1/\epsilon)$ rates for learning VIs that satisfy strong monotonicity, a generalization of strong convexity. Specifically, we demonstrate that standard stability-based generalization arguments for convex minimization extend directly to VIs when the domain admits a small covering, or when the operator is integrable and suboptimality is measured by potential functions; such as when finding equilibria in multi-player games.

While the pretraining of Foundation Models (FMs) for remote sensing (RS) imagery is on the rise, models remain restricted to a few hundred million parameters. Scaling models to billions of parameters has been shown to yield unprecedented benefits including emergent abilities, but requires data scaling and computing resources typically not available outside industry R&D labs. In this work, we pair high-performance computing resources including Frontier supercomputer, America's first exascale system, and high-resolution optical RS data to pretrain billion-scale FMs. Our study assesses performance of different pretrained variants of vision Transformers across image classification, semantic segmentation and object detection benchmarks, which highlight the importance of data scaling for effective model scaling. Moreover, we discuss construction of a novel TIU pretraining dataset, model initialization, with data and pretrained models intended for public release. By discussing technical challenges and details often lacking in the related literature, this work is intended to offer best practices to the geospatial community toward efficient training and benchmarking of larger FMs.

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

The use of autonomous systems in medical evacuation (MEDEVAC) scenarios is promising, but existing implementations overlook key insights from human-robot interaction (HRI) research. Studies on human-machine teams demonstrate that human perceptions of a machine teammate are critical in governing the machine's performance. Here, we present a mixed factorial design to assess human perceptions of a MEDEVAC robot in a simulated evacuation scenario. Participants were assigned to the role of casualty (CAS) or bystander (BYS) and subjected to three within-subjects conditions based on the MEDEVAC robot's operating mode: autonomous-slow (AS), autonomous-fast (AF), and teleoperation (TO). During each trial, a MEDEVAC robot navigated an 11-meter path, acquiring a casualty and transporting them to an ambulance exchange point while avoiding an idle bystander. Following each trial, subjects completed a questionnaire measuring their emotional states, perceived safety, and social compatibility with the robot. Results indicate a consistent main effect of operating mode on reported emotional states and perceived safety. Pairwise analyses suggest that the employment of the AF operating mode negatively impacted perceptions along these dimensions. There were no persistent differences between casualty and bystander responses.

Local Differential Privacy (LDP) offers strong privacy guarantees without requiring users to trust external parties. However, LDP applies uniform protection to all data features, including less sensitive ones, which degrades performance of downstream tasks. To overcome this limitation, we propose a Bayesian framework, Bayesian Coordinate Differential Privacy (BCDP), that enables feature-specific privacy quantification. This more nuanced approach complements LDP by adjusting privacy protection according to the sensitivity of each feature, enabling improved performance of downstream tasks without compromising privacy. We characterize the properties of BCDP and articulate its connections with standard non-Bayesian privacy frameworks. We further apply our BCDP framework to the problems of private mean estimation and ordinary least-squares regression. The BCDP-based approach obtains improved accuracy compared to a purely LDP-based approach, without compromising on privacy.