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We introduce SynFormer, a generative modeling framework designed to efficiently explore and navigate synthesizable chemical space. Unlike traditional molecular generation approaches, we generate synthetic pathways for molecules to ensure that designs are synthetically tractable. By incorporating a scalable transformer architecture and a diffusion module for building block selection, SynFormer surpasses existing models in synthesizable molecular design. We demonstrate SynFormer's effectiveness in two key applications: (1) local chemical space exploration, where the model generates synthesizable analogs of a reference molecule, and (2) global chemical space exploration, where the model aims to identify optimal molecules according to a black-box property prediction oracle. Additionally, we demonstrate the scalability of our approach via the improvement in performance as more computational resources become available. With our code and trained models openly available, we hope that SynFormer will find use across applications in drug discovery and materials science.

When training transformers on graph-structured data, incorporating information about the underlying topology is crucial for good performance. Topological masking, a type of relative position encoding, achieves this by upweighting or downweighting attention depending on the relationship between the query and keys in a graph. In this paper, we propose to parameterise topological masks as a learnable function of a weighted adjacency matrix -- a novel, flexible approach which incorporates a strong structural inductive bias. By approximating this mask with graph random features (for which we prove the first known concentration bounds), we show how this can be made fully compatible with linear attention, preserving $\mathcal{O}(N)$ time and space complexity with respect to the number of input tokens. The fastest previous alternative was $\mathcal{O}(N \log N)$ and only suitable for specific graphs. Our efficient masking algorithms provide strong performance gains for tasks on image and point cloud data, including with $>30$k nodes.

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

AI-based systems such as language models can replicate and amplify social biases reflected in their training data. Among other questionable behavior, this can lead to LM-generated text--and text suggestions--that contain normatively inappropriate stereotypical associations. In this paper, we consider the question of how "debiasing" a language model impacts stories that people write using that language model in a predictive text scenario. We find that (n=414), in certain scenarios, language model suggestions that align with common social stereotypes are more likely to be accepted by human authors. Conversely, although anti-stereotypical language model suggestions sometimes lead to an increased rate of anti-stereotypical stories, this influence is far from sufficient to lead to "fully debiased" stories.

The Beryllium Electron capture in Superconducting Tunnel junctions (BeEST) experiment searches for evidence of heavy neutrino mass eigenstates in the nuclear electron capture decay of $^7$Be by precisely measuring the recoil energy of the $^7$Li daughter. In Phase-III, the BeEST experiment has been scaled from a single superconducting tunnel junction (STJ) sensor to a 36-pixel array to increase sensitivity and mitigate gamma-induced backgrounds. Phase-III also uses a new continuous data acquisition system that greatly increases the flexibility for signal processing and data cleaning. We have developed procedures for signal processing and spectral fitting that are sufficiently robust to be automated for large data sets. This article presents the optimized procedures before unblinding the majority of the Phase-III data set to search for physics beyond the standard model.

Cat qubits, a type of bosonic qubit encoded in a harmonic oscillator, can exhibit an exponential noise bias against bit-flip errors with increasing mean photon number. Here, we focus on cat qubits stabilized by two-photon dissipation, where pairs of photons are added and removed from a harmonic oscillator by an auxiliary, lossy buffer mode. This process requires a large loss rate and strong nonlinearities of the buffer mode that must not degrade the coherence and linearity of the oscillator. In this work, we show how to overcome this challenge by coloring the loss environment of the buffer mode with a multi-pole filter and optimizing the circuit to take into account additional inductances in the buffer mode. Using these techniques, we achieve near-ideal enhancement of cat-qubit bit-flip times with increasing photon number, reaching over $0.1$ seconds with a mean photon number of only $4$. Concurrently, our cat qubit remains highly phase coherent, with phase-flip times corresponding to an effective lifetime of $T_{1,\text{eff}} \simeq 70$ $\mu$s, comparable with the bare oscillator lifetime. We achieve this performance even in the presence of an ancilla transmon, used for reading out the cat qubit states, by engineering a tunable oscillator-ancilla dispersive coupling. Furthermore, the low nonlinearity of the harmonic oscillator mode allows us to perform pulsed cat-qubit stabilization, an important control primitive, where the stabilization can remain off for a significant fraction (e.g., two thirds) of a $3~\mathrm{\mu s}$ cycle without degrading bit-flip times. These advances are important for the realization of scalable error-correction with cat qubits, where large noise bias and low phase-flip error rate enable the use of hardware-efficient outer error-correcting codes., Comment: Comments welcome!

Quantum chromodynamics (QCD) has yielded a vast literature spanning distinct phenomena. We construct a corpus of papers and build a generative model. This model holds promise for accelerating the capability of scientists to consolidate their knowledge of QCD by the ability to generate and validate scientific works in the landscape of works related to QCD and similar problems in HEP. Furthermore, we discuss challenges and future directions of using large language models to integrate our scientific knowledge about QCD through the automated generation of explanatory scientific texts., Comment: 3 pages, 0 figure, conference proceedings

Studies have shown that the morphologies of galaxies are substantially transformed following coalescence after a merger, but post-mergers are notoriously difficult to identify, especially in imaging that is shallow or low-resolution. We train convolutional neural networks (CNNs) to identify simulated post-merger galaxies in a range of image qualities, modelled after five real surveys: the Sloan Digital Sky Survey (SDSS), the Dark Energy Camera Legacy Survey (DECaLS), the Canada-France Imaging Survey (CFIS), the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP), and the Legacy Survey of Space and Time (LSST). Holding constant all variables other than imaging quality, we present the performance of the CNNs on reserved test set data for each image quality. The success of CNNs on a given dataset is found to be sensitive to both imaging depth and resolution. We find that post-merger recovery generally increases with depth, but that limiting 5 sigma point-source depths in excess of ~25 mag, similar to what is achieved in CFIS, are only marginally beneficial. Finally, we present the results of a cross-survey inference experiment, and find that CNNs trained on a given image quality can sometimes be applied to different imaging data to good effect. The work presented here therefore represents a useful reference for the application of CNNs for merger searches in both current and future imaging surveys., Comment: 19 pages, 17 figures. Accepted for publication in MNRAS

Fast Radio Bursts (FRBs) are millisecond-duration events detected from beyond the Milky Way. FRB emission characteristics favor highly magnetized neutron stars, or magnetars, as the sources, as evidenced by FRB-like bursts from a galactic magnetar, and the star-forming nature of FRB host galaxies. However, the processes that produce FRB sources remain unknown. Although galactic magnetars are often linked to core-collapse supernovae (CCSNe), it's uncertain what determines which supernovae result in magnetars. The galactic environments of FRB sources can be harnessed to probe their progenitors. Here, we present the stellar population properties of 30 FRB host galaxies discovered by the Deep Synoptic Array. Our analysis shows a significant deficit of low-mass FRB hosts compared to the occurrence of star-formation in the universe, implying that FRBs are a biased tracer of star-formation, preferentially selecting massive star-forming galaxies. This bias may be driven by galaxy metallicity, which is positively correlated with stellar mass. Metal-rich environments may favor the formation of magnetar progenitors through stellar mergers, as higher metallicity stars are less compact and more likely to fill their Roche lobes, leading to unstable mass transfer. Although massive stars do not have convective interiors to generate strong magnetic fields by dynamo, merger remnants are thought to have the requisite internal magnetic-field strengths to result in magnetars. The preferential occurrence of FRBs in massive star-forming galaxies suggests that CCSN of merger remnants preferentially forms magnetars., Comment: Accepted for publication in Nature. The final version will be published by the journal

Approximately half of the Universe's dark matter resides in collapsed halos; significantly less than half of the baryonic matter (protons and neutrons) remains confined to halos. A small fraction of baryons are in stars and the interstellar medium within galaxies. The lion's share are diffuse (less than $10^{-3}$ cm$^{-3}$) and ionized (neutral fraction less than $10^{-4}$), located in the intergalactic medium (IGM) and in the halos of galaxy clusters, groups, and galaxies. The quantity and spatial distribution of this diffuse ionized gas is notoriously difficult to measure, but has wide implications for galaxy formation, astrophysical feedback, and precision cosmology. Recently, the dispersion of extragalactic Fast Radio Bursts (FRBs) has been used to measure the total content of cosmic baryons. However, past efforts had modest samples and methods that cannot discriminate between IGM and halo gas, which is critical for studying feedback and for observational cosmology. Here, we present a large cosmological sample of FRB sources localized to their host galaxies. We have robustly partitioned the missing baryons into the IGM, galaxy clusters, and galaxies, providing a late-Universe measurement of the total baryon density of $\Omega_b h_{70}$=0.049$\pm$0.003. Our results indicate efficient feedback processes that can expel gas from galaxy halos and into the intergalactic medium, agreeing with the enriched cosmic web scenario seen in cosmological simulations. The large diffuse baryon fraction that we have measured disfavours bottom-heavy stellar initial mass functions, which predict a large total stellar density, $\Omega_*$.

A vast program of determinations of the strong coupling $\alpha_\mathrm{S}$ is being undertaken by CMS. These measurements exploit several QCD-dominated processes that are sensitive to $\alpha_\mathrm{S}$, and present different theoretical and experimental challenges. A review of the current public results and perspective is given., Comment: 6 pages, 2 figures, conference proceedings

A tight $\ell$-cycle minus an edge $C_\ell^-$ is the $3$-graph on the vertex set $[\ell]$, where any three consecutive vertices in the string $123\ldots\ell 1$ form an edge. We show that for every $\ell\ge 5$, $\ell$ not divisible by $3$, the extremal number is $ ex\left(C_\ell^-,n\right)=\tfrac1{24}n^3+O(n\ln n)=\left(\tfrac14+o(1)\right){n\choose 3}. $ We determine the extremal graph up to $O(n)$ edge edits.

In this paper, we explore accelerating Hamiltonian ground state energy calculation on NISQ devices. We suggest using search-based methods together with machine learning to accelerate quantum algorithms, exemplified in the Quantum Eigensolver use case. We trained two small models on classically mined data from systems with up to 16 qubits, using XGBoost's Python regressor. We evaluated our preliminary approach on 20-, 24- and 28-qubit systems by optimising the Eigensolver's hyperparameters. These models predict hyperparameter values, leading to a 0.13\%-0.15\% reduction in error when tested on 28-qubit systems. However, due to inconclusive results with 20- and 24-qubit systems, we suggest further examination of the training data based on Hamiltonian characteristics. In future work, we plan to train machine learning models to optimise other aspects or subroutines of quantum algorithm execution beyond its hyperparameters.

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

In this work, the Divergent Component of Motion (DCM) method is expanded to include angular coordinates for the first time. This work introduces the idea of spatial DCM, which adds an angular objective to the existing linear DCM theory. To incorporate the angular component into the framework, a discussion is provided on extending beyond the linear motion of the Linear Inverted Pendulum model (LIPM) towards the Single Rigid Body model (SRBM) for DCM. This work presents the angular DCM theory for a 1D rotation, simplifying the SRBM rotational dynamics to a flywheel to satisfy necessary linearity constraints. The 1D angular DCM is mathematically identical to the linear DCM and defined as an angle which is ahead of the current body rotation based on the angular velocity. This theory is combined into a 3D linear and 1D angular DCM framework, with discussion on the feasibility of simultaneously achieving both sets of objectives. A simulation in MATLAB and hardware results on the TORO humanoid are presented to validate the framework's performance.

The C IV doublet in the UV has long been associated with accretion in T Tauri stars. However, it is still unclear where and how the lines are formed. Here, we present a new C IV line model based on the currently available accretion shock and accretion flow models. We assume axisymmetric, dipolar accretion flows with different energy fluxes and calculate the properties of the accretion shock. We use Cloudy to obtain the carbon level populations and calculate the emerging line profiles assuming a plane-parallel geometry near the shock. Our model generally reproduces the intensities and shapes of the C IV emission lines observed from T Tauri stars. We find that the narrow component is optically thin and originates in the postshock, while the broad component is optically thick and emerges from the preshock. We apply our model to seven T Tauri stars from the Hubble Ultraviolet Legacy Library of Young Stars as Essential Standards Director's Discretionary program (ULLYSES), for which consistently determined accretion shock properties are available. We can reproduce the observations of four stars, finding that the accretion flows are carbon-depleted. We also find that the chromospheric emission accounts for less than 10 percent of the observed C IV line flux in accreting T Tauri stars. This work paves the way toward a better understanding of hot line formation and provides a potential probe of abundances in the inner disk., Comment: Accepted for publication in the Astrophysical Journal, 26 pages, 14 figures

This paper presents a comprehensive study on the scalability challenges and opportunities in quantum communication networks, with the goal of determining parameters that impact networks most as well as the trends that appear when scaling networks. We design simulations of quantum networks comprised of router nodes made up of trapped-ion qubits, separated by quantum repeaters in the form of Bell State Measurement (BSM) nodes. Such networks hold the promise of securely sharing quantum information and enabling high-power distributed quantum computing. Despite the promises, quantum networks encounter scalability issues due to noise and operational errors. Through a modular approach, our research aims to surmount these challenges, focusing on effects from scaling node counts and separation distances while monitoring low-quality communication arising from decoherence effects. We aim to pinpoint the critical features within networks essential for advancing scalable, large-scale quantum computing systems. Our findings underscore the impact of several network parameters on scalability, highlighting a critical insight into the trade-offs between the number of repeaters and the quality of entanglement generated. This paper lays the groundwork for future explorations into optimized quantum network designs and protocols., Comment: 10 pages, 11 figures

Telescope missions are currently being designed which will make direct imaging of habitable exoplanets possible in the near future, and studies are needed to quantify the detectability of biosignature features in the planet's reflectance spectrum. We simulated the detectability of a NIR-absorbing surface biosignature feature with simulated observations of the nearby exoplanet Proxima Centauri b. We modeled a biosignature spectral feature with a reflectance spectrum based on an anoxygenic photosynthetic bacterial species that has strong absorption at 1 um, which could make it well suited for life on an M-dwarf hosted planet. We modeled the distribution of this organism across the planet's surface based on climate states from a 3D General Circulation Model (GCM), which were Archean and Proterozoic-like exo-Earth analogues. We included the GCM runs' prognostically simulated water clouds and added organic haze into the Archean-like atmospheres. We simulated observations of these Proxima Centauri b scenarios with the LUVOIR-A and B telescope concepts, with LUVOIR-B serving as a proxy to the planned Habitable Worlds Observatory (HWO). We calculated integration times necessary to detect the biosignature, and found that it would be detectable on Proxima Centauri b if the organism is moderately abundant (greater than a 1-4% global surface area coverage), as long as the atmosphere is transmitting in the wavelength range under consideration. Small amounts of methane, clouds, and haze do not greatly impede detectability. We found preliminary evidence that such a biosignature would be detectable on exoplanets within 15 pc, but further investigations are needed to corroborate this., Comment: 31 pages, 9 figures, 2 tables

In this paper, we develop a class of interacting particle Langevin algorithms to solve inverse problems for partial differential equations (PDEs). In particular, we leverage the statistical finite elements (statFEM) formulation to obtain a finite-dimensional latent variable statistical model where the parameter is that of the (discretised) forward map and the latent variable is the statFEM solution of the PDE which is assumed to be partially observed. We then adapt a recently proposed expectation-maximisation like scheme, interacting particle Langevin algorithm (IPLA), for this problem and obtain a joint estimation procedure for the parameters and the latent variables. We consider three main examples: (i) estimating the forcing for linear Poisson PDE, (ii) estimating the forcing for nonlinear Poisson PDE, and (iii) estimating diffusivity for linear Poisson PDE. We provide computational complexity estimates for forcing estimation in the linear case. We also provide comprehensive numerical experiments and preconditioning strategies that significantly improve the performance, showing that the proposed class of methods can be the choice for parameter inference in PDE models., Comment: 23 pages, 6 figures

We use the cosmological magneto-hydrodynamical simulation TNG50 to study the relationship between black hole feedback, the presence of stellar bars, and star formation quenching in Milky Way-like disc galaxies. Of our sample of 198 discs, about 63 per cent develop stellar bars that last until z=0. After the formation of their bars, the majority of these galaxies develop persistent 3-15 kpc wide holes in the centres of their gas discs. Tracking their evolution from z=4 to 0, we demonstrate that barred galaxies tend to form within dark matter haloes that become centrally disc dominated early on (and are thus unstable to bar formation) whereas unbarred galaxies do not; barred galaxies also host central black holes that grow more rapidly than those of unbarred galaxies. As a result, most barred galaxies eventually experience kinetic wind feedback that operates when the mass of the central supermassive black hole exceeds $M_{BH} > 10^8 M_{\odot}$. This feedback ejects gas from the central disc into the circumgalactic medium and rapidly quenches barred galaxies of their central star formation. If kinetic black hole feedback occurs in an unbarred disc it suppresses subsequent star formation and inhibits its growth, stabilising the disc against future bar formation. Consequently, most barred galaxies develop black hole-driven gas holes, though a gas hole alone does not guarantee the presence of a stellar bar. This subtle relationship between black hole feedback, cold gas disc morphology, and stellar bars may provide constraints on subgrid physics models for supermassive black hole feedback., Comment: 9 pages, 7 figures, submitted to MNRAS. Comments welcome

Highly inclined (edge-on) disk galaxies offer the unique perspective to constrain their intrinsic flattening, $c/a$, where $c$ and $a$ are respectively the vertical and long radial axes of the disk measured at suitable stellar densities. The ratio $c/a$ is a necessary quantity in the assessment of galaxy inclinations, three-dimensional structural reconstructions, total masses, as well as a constraint to galaxy formation models. 3.6 micron maps of 133 edge-on spiral galaxies from the Spitzer Survey of Stellar Structure in Galaxies (S4G) and its early-type galaxy extension are used to revisit the assessment of $c/a$ free from dust extinction and away from the influence of a stellar bulge. We present a simple definition of $c/a$ and explore trends with other galactic physical parameters: total stellar mass, concentration index, total HI mass, mass of the central mass concentration, circular velocity, model-dependent scales, as well as Hubble type. Other than a dependence on early/late Hubble types, and a related trend with light concentration, no other parameters were found to correlate with the intrinsic flattening of spiral galaxies. The latter is mostly constant with $\langle c/a \rangle$ = 0.124 $\pm$ 0.001 (stat) $\pm$ 0.033 (intrinsic/systematic) and greater for earlier types., Comment: 27 full pages, 14 figures, 6 tables, 1 appendix. Submitted to AAS Journals. Comments welcome

With advancements in computer vision and deep learning, video-based human action recognition (HAR) has become practical. However, due to the complexity of the computation pipeline, running HAR on live video streams incurs excessive delays on embedded platforms. This work tackles the real-time performance challenges of HAR with four contributions: 1) an experimental study identifying a standard Optical Flow (OF) extraction technique as the latency bottleneck in a state-of-the-art HAR pipeline, 2) an exploration of the latency-accuracy tradeoff between the standard and deep learning approaches to OF extraction, which highlights the need for a novel, efficient motion feature extractor, 3) the design of Integrated Motion Feature Extractor (IMFE), a novel single-shot neural network architecture for motion feature extraction with drastic improvement in latency, 4) the development of RT-HARE, a real-time HAR system tailored for embedded platforms. Experimental results on an Nvidia Jetson Xavier NX platform demonstrated that RT-HARE realizes real-time HAR at a video frame rate of 30 frames per second while delivering high levels of recognition accuracy.

We study transits of several ``hot Jupiter'' systems - including WASP-12 b, WASP-43 b, WASP-103 b, HAT-P-23 b, KELT-16 b, WD 1856+534 b, and WTS-2 b - with the goal of detecting tidal orbital decay and extending the baselines of transit times. We find no evidence of orbital decay in any of the observed systems except for that of the extensively studied WASP-12 b. Although the orbit of WASP-12 b is unequivocally decaying, we find no evidence for acceleration of said orbital decay, with measured $\ddot{P} = (-7 \pm 8) \times 10^{-14} \rm ~s^{-1}$, against the expected acceleration decay of $\ddot{P} \approx -10^{-23} \rm ~s^{-1}$. In the case of WD 1856+534 b, there is a tentative detection of orbital growth with $\dot{P} = (5.0 \pm 1.5) \times 10^{-10}$. While statistically significant, we err on the side of caution and wait for longer follow-up observations to consider the measured $\dot{P}$ real. For most systems, we provide a 95\%-confidence lower limit on the tidal quality factor, $Q_\star'$. The possibility of detecting orbital decay in hot Jupiters via long-term radial velocity (RV) measurements is also explored. We find that $\sim 1 \rm ~m~s^{-1}$ precision in RVs will be required to detect orbital decay of WASP-12 b with only 3 yr of observations. Currently available RV measurements and precision are unable to detect orbital decay in any of the systems studied here., Comment: 14 pages, 11 figures; Accepted in MNRAS on 2024 August 30. Received 2024 August 29; in original form 2024 February 13

Introducing superconductivity in topological materials can lead to innovative electronic phases and device functionalities. Here, we present a new strategy for quantum engineering of superconducting junctions in moire materials through direct, on-chip, and fully encapsulated 2D crystal growth. We achieve robust and designable superconductivity in Pd-metalized twisted bilayer molybdenum ditelluride (MoTe2) and observe anomalous superconducting effects in high-quality junctions across ~ 20 moire cells. Surprisingly, the junction develops enhanced, instead of weakened, superconducting behaviors, exhibiting fluctuations to a higher critical magnetic field compared to its adjacent Pd7MoTe2 superconductor. Additionally, the critical current further exhibits a striking V-shaped minimum at zero magnetic field. These features are unexpected in conventional Josephson junctions and indeed absent in junctions of natural bilayer MoTe2 created using the same approach. We discuss implications of these observations, including the possible formation of mixed even- and odd-parity superconductivity at the moire junctions. Our results also demonstrate a pathway to engineer and investigate superconductivity in fractional Chern insulators., Comment: 25 pages, 5 main article figures and 10 supplementary figures

The macular pigment (MP) in the radially-oriented Henle fibers that surround the foveola enables the ability to perceive the orientation of polarized blue light through an entoptic phenomena known as the Haidinger's brush. The MP has been linked to eye diseases and central field dysfunctions, most notably age-related macular degeneration (AMD), a globally leading cause of irreversible blindness. Recent integration of structured light techniques into vision science has allowed for the development of more selective and versatile entoptic probes of eye health that provide interpretable thresholds. For example, it enabled the use of variable spatial frequencies and arbitrary obstructions in the presented stimuli. Additionally, it expanded the 2{\deg} retinal eccentricity extent of the Haidinger's brush to 5{\deg} for a similar class of fringe-based stimuli. In this work, we develop a spatiotemporal sensitivity model that maps perceptual thresholds of entoptic phenomenon to the underlying MP structure that supports its perception. We therefore selectively characterize the circularly-oriented macular pigment optical density (coMPOD) rather than total MPOD as typically measured, providing an additional quantification of macular health. A study was performed where the retinal eccentricity thresholds were measured for five structured light stimuli with unique spatiotemporal frequencies. The results from fifteen healthy young participants indicate that the coMPOD is inversely proportional to retinal eccentricity in the range of 1.5{\deg} to 5.5{\deg}. Good agreement between the model and the collected data is found with a Pearson $\chi^2$ fit statistic of 0.06. The presented techniques can be applied in novel early diagnostic tests for a variety of diseases related to macular degeneration such as AMD, macular telangiectasia, and pathological myopia.

Stellar evolution theory predicts that the Sun was fainter in the past, which can pose difficulties for understanding Earth's climate history. One proposed solution to this Faint Young Sun problem is a more luminous Sun in the past. In this paper, we address the robustness of the solar luminosity history using the YREC code to compute solar models including rotation, magnetized winds, and the associated mass loss. We present detailed solar models, including their evolutionary history, which are in excellent agreement with solar observables. Consistent with prior standard models, we infer a high solar metal content. We provide predicted X-ray luminosities and rotation histories for usage in climate reconstructions and activity studies. We find that the Sun's luminosity deviates from the standard solar model trajectory by at most 0.5% during the Archean (corresponding to a radiative forcing of 0.849 W m$^{-2}$). The total mass loss experienced by solar models is modest because of strong feedback between mass and angular momentum loss. We find a maximum mass loss of $1.35 \times 10^{-3} M_\odot$ since birth, at or below the level predicted by empirical estimates. The associated maximum luminosity increase falls well short of the level necessary to solve the FYS problem. We present compilations of paleotemperature and CO$_2$ reconstructions. 1-D "inverse" climate models demonstrate a mismatch between the solar constant needed to reach high temperatures (e.g. 60-80 $^{\circ}$C) and the narrow range of plausible solar luminosities determined in this study. Maintaining a temperate Earth, however, is plausible given these conditions., Comment: 18 pages, 14 figures

We discuss the second-order differential uniformity of vectorial Boolean functions. The closely related notion of second-order zero differential uniformity has recently been studied in connection to resistance to the boomerang attack. We prove that monomial functions with univariate form $x^d$ where $d=2^{2k}+2^k+1$ and $\gcd(k,n)=1$ have optimal second-order differential uniformity. Computational results suggest that, up to affine equivalence, these might be the only optimal cubic power functions. We begin work towards generalising such conditions to all monomial functions of algebraic degree 3. We also discuss further questions arising from computational results.

Clustering is an unsupervised learning task that aims to partition data into a set of clusters. In many applications, these clusters correspond to real-world constructs (e.g., electoral districts, playlists, TV channels) whose benefit can only be attained by groups when they reach a minimum level of representation (e.g., 50\% to elect their desired candidate). In this paper, we study the k-means and k-medians clustering problems with the additional constraint that each group (e.g., demographic group) must have a minimum level of representation in at least a given number of clusters. We formulate the problem through a mixed-integer optimization framework and present an alternating minimization algorithm, called MiniReL, that directly incorporates the fairness constraints. While incorporating the fairness criteria leads to an NP-Hard assignment problem within the algorithm, we provide computational approaches that make the algorithm practical even for large datasets. Numerical results show that the approach is able to create fairer clusters with practically no increase in the clustering cost across standard benchmark datasets., Comment: arXiv admin note: text overlap with arXiv:2302.03151

We introduce a version of Koszul duality for categories, which extends the Koszul duality of operads and right modules. We demonstrate that the derivatives which appear in Weiss calculus (with values in spectra) form a right module over the Koszul dual of the category of vector spaces and orthogonal surjections, resolving conjectures of Arone--Ching and Espic. Using categorical Fourier transforms, we then classify Weiss towers. In particular, we describe the $n$-th polynomial approximation as a pullback of the $(n-1)$-st polynomial approximation along a ``generalized norm map''.

There are holographic superconformal theories in all dimensions between two and six which allow arbitrary tree-level four-point functions to be fixed by basic consistency conditions. Although Mellin space is usually the most efficient setting for imposing these contraints, four-point functions in two dimensions have thus far been an exception due to their more intricate dependence on the conformal cross-ratios. In this paper, we introduce a simple fix which exploits the relation between a parity-odd conformal block in two dimensions and a parity-even conformal block in four dimensions. We then apply the resulting toolkit to a study of the paradigmatic holographic theory in two dimensions which is the D1-D5 CFT. For correlators involving Kaluza-Klein modes of the tensor multiplet, this analysis reproduces results which were previously obtained using hidden conformal symmetry. With four Kaluza-Klein modes of the graviton multiplet, it yields new results including a compact formula for the correlators of all pairwise identical operators, Comment: 30 + 19 pages, 3 figures. Fixed typos and improved explanations

ARINC 429 is a ubiquitous data bus for civil avionics, enabling reliable communication between devices from disparate manufacturers. However, ARINC 429 lacks any form of encryption or authentication, making it an inherently insecure communication protocol and rendering any connected avionics vulnerable to a range of attacks. We constructed a hardware-in-the-loop simulator with ARINC 429 buses, explored these vulnerabilities, and identified their potential to deny, degrade, or disrupt aircraft capabilities. We performed a denial-of-service attack against a multi-function display via a compromised ARINC 429 bus using commercially available tools, which succeeded in disabling important navigational aids. This proven attack on physical avionics illustrates the risk inherent in ARINC 429 and the need for the ability to detect these attacks. One potential mitigation is an intrusion detection system (IDS) trained on data collected from the electrical properties of the physical bus. Although previous research has demonstrated the feasibility of an IDS on an ARINC 429 bus, no IDS has been trained on data generated by avionics hardware. To facilitate this, we recorded voltage traces and message history generated by avionics and adversarial devices on the ARINC 429 bus. To the best of our knowledge, this is the first publicly available collection of hardware-generated ARINC 429 signal data., Comment: 7 pages, 3 figures. Intended for publication in IEEE Transactions on Aerospace and Electronic Systems

Designing synthetically accessible molecules and recommending analogs to unsynthesizable molecules are important problems for accelerating molecular discovery. We reconceptualize both problems using ideas from program synthesis. Drawing inspiration from syntax-guided synthesis approaches, we decouple the syntactic skeleton from the semantics of a synthetic tree to create a bilevel framework for reasoning about the combinatorial space of synthesis pathways. Given a molecule we aim to generate analogs for, we iteratively refine its skeletal characteristics via Markov Chain Monte Carlo simulations over the space of syntactic skeletons. Given a black-box oracle to optimize, we formulate a joint design space over syntactic templates and molecular descriptors and introduce evolutionary algorithms that optimize both syntactic and semantic dimensions synergistically. Our key insight is that once the syntactic skeleton is set, we can amortize over the search complexity of deriving the program's semantics by training policies to fully utilize the fixed horizon Markov Decision Process imposed by the syntactic template. We demonstrate performance advantages of our bilevel framework for synthesizable analog generation and synthesizable molecule design. Notably, our approach offers the user explicit control over the resources required to perform synthesis and biases the design space towards simpler solutions, making it particularly promising for autonomous synthesis platforms.

In the dynamics of atoms and molecules at metal surfaces, electron-hole pair excitations can play a crucial role. In the case of hyperthermal hydrogen atom scattering, they lead to nonadiabatic energy loss and highly inelastic scattering. Molecular dynamics with electronic friction simulations where friction is computed under an isotropic homogeneous electron gas approximation have previously shown good agreement with measured kinetic energy loss distributions, suggesting that this level of theoretical description is sufficient to describe nonadiabatic effects of atomic scattering. In this work, we show that similar agreement with room temperature experiments can also be achieved with friction derived from density functional theory linear response calculations. The apparent agreement of the homogeneous electron gas approximation with experiment arises from a fortuitous cancellation of errors where friction is overestimated close to the surface and the spin transition is neglected. Only for scattering at low temperatures can both approximations be distinguished and differences rationalised in terms of the number of bounces of the atom on the surface. We identify the signatures of nonadiabatic energy loss of different levels of theory, which future low-temperature scattering experiments will be able to measure., Comment: 40 pages, 14 figures, includes supplemental material