Human-Computer Interaction
A Case Study in Accessible Redesign of a Wastewater Dashboard
Tingying He, Jake Wagoner, Md Rahat-uz- Zaman et al. (+6 more)
Public health dashboards communicate data that can inform important decisions, but they often raise accessibility challenges. We present a case study redesigning the Utah Wastewater Surveillance System dashboard to improve accessibility and usability across desktop and mobile settings. The redesign was informed by WCAG and developed iteratively with Utah DHHS collaborators, and we collected feedback on the final design from an external blind researcher. Our case study highlights that accessible dashboard design requires aligning accessibility guidelines with user needs, stakeholder workflows, and technical constraints. It also suggests that simple, targeted technical solutions tailored to the existing environment can provide practical value for dashboard redesign in government contexts.
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All authors Tingying He, Jake Wagoner, Md Rahat-uz- Zaman, Md Dilshadur Rahman, Willy Ray, George G. Vega Yon, Matthew Samore, Alexander Lex, Paul Rosen
Artificial Intelligence
MedGate-Fusion: Integrating First-Encounter Semantic Narratives and Physiological Biomarkers for Prospective Stroke Risk Stratification
Hemn Khdr, Mohammad Noaeen, Karim Keshavjee et al. (+2 more)
Prospective stroke risk stratification in primary care is challenging because early risk signals are distributed across routine biomarkers and unstructured clinical narratives. We propose MedGate-Fusion, a multi-modal gated architecture that integrates
transformer-based
embeddings of first-encounter narratives with ten routinely recorded risk markers. We used electronic medical record data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). Starting from 808,921 encounter-level observations, we constructed a first-encounter cohort and retained 102,736 unique patient records with non-empty narratives and sufficient data to evaluate a five-year stroke outcome. To reduce explicit target leakage from diagnostic mentions in notes, we applied dictionary-based redaction of stroke-related terms prior to semantic encoding.
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All authors Hemn Khdr, Mohammad Noaeen, Karim Keshavjee, Aziz Guergachi, Zahra Shakeri
Robotics
Beyond the Flat Seafloor: A Closed-Form Two-View Constraint to Aid Sidescan Sonar Reconstruction
Kalin Norman and Joshua G. Mangelson
Sidescan sonar is a common sensor for both manned and autonomous marine exploration and mapping, yet very few methods build upon or exploit the
geometric projection model of the sensor. As sidescan sonar is limited to a 1D range measurement, many approximations are frequently used, including the long-standing assumption of a flat seafloor. Rather than make similar approximations, this paper focuses on a multi-view geometry based approach and formalizes a two-view
geometric constraint and proves that a shared feature is constrained to a locus within the intersection of a sphere and a plane. In addition, we characterize what governs the size of the ambiguity locus through Monte Carlo simulation that is grounded in real aperture and mounting geometry for both a surface vessel and an
underwater vehicle. We translate additional simulations of relative trajectories for both vehicle platforms into concrete survey-planning guidance. Our results show that locus length is strongly governed by elevation misalignment, and peaks at a moderate oblique crossing angle of approximately 20 degrees, with minimal locus lengths obtained at near parallel and anti-parallel passes.
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Computer Vision
RULER: Instance-aware Rubric Rewards for SVG Generation
Hangyu Ran, Yuhao Zheng, Yingying Zhang et al. (+2 more)
Generating Scalable Vector Graphics (SVG) code from natural-
language instructions is an open-ended task without absolute visual ground truth, leaving both evaluation and policy optimization without a faithful signal. Scalar metrics (CLIP, Aesthetic) calibrated on natural images transfer poorly to stylized vector content, and reusing them as RL rewards triggers reward hacking. We address both limitations with rubric-based scoring. We first establish empirically that prompting a
vision-
language judge with a multi-axis rubric correlates with human judgments far better than scalar metrics, both across samples and within instructions. Building on this finding, we introduce RULER (Instance-aware Rubric Rewards for Reinforcement Learning), which converts each instruction into an instance-aware rubric of six items spanning semantic, visual, and stylistic axes; a judge VLM scores rendered rollouts item-by-item, and the weighted satisfactions form a fine-grained reward optimized via Group Relative Policy Optimization. Because the rubric is derived from text alone, RULER requires neither paired SVG ground truth nor human preference labels. On MMSVG-Illustration and MMSVG-Icon, RULER lifts the rubric score from 0.432/0.395 to 0.693/0.683, surpassing dedicated SVG specialists and matching the substantially larger DeepSeek-V3, with ablations identifying rubric design as the active lever for RL on open-ended SVG generation. The project page is available at https://hangyuran.github.io/RULER/.
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All authors Hangyu Ran, Yuhao Zheng, Yingying Zhang, Kevin Qinghong Lin, Han Peng
Mathematical Physics
Eigenvectors and Eigenvalues of $p$-Curvature Operators for KZ–type Differential Connections
Vitaly Tarasov and Alexander Varchenko
A KZ-type connection in characteristic $p$ has $p$-
curvature operators. These operators are commuting endomorphisms of the connection. We present a Bethe-ansatz-type construction of their eigenvectors and
eigenvalues. The eigenvector is constructed as the value of a characteristic $p$ hypergeometric integral and extends to a flat eigensection over the Frobenius neighborhood of the corresponding Bethe point.
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Differential Geometry
Futaki invariant on lcK manifolds with potential
Giacomo Perri
We prove that the Futaki invariant introduced by Futaki, Hattori, and Ornea vanishes identically on every compact locally conformally Kähler
manifold with potential, thereby answering a question raised by Ornea and Verbitsky.
Computer Vision
ImIR: Image-Instruction Tuning for All-in-One Image Restoration
Süleyman Aslan, Görkay Aydemir, M{\i}sra Yavuz et al. (+5 more)
Degradations vary widely across images, so a practical restoration system has to handle many degradation types with one model. A recent and effective recipe adapts a large pretrained image-editing model to restoration using a small low-rank adapter with a text
prompt. We replace that
prompt with an instruction derived from the degraded image itself. The image reaches the editor through two paths: its structure comes from the model's VAE, and its semantic instruction comes from a lightweight
token mapper that shifts the degraded image's
vision-
language embedding toward the
embedding a clean image would produce. Because the instruction is a continuous vector, scaling it yields a family of valid restorations for tasks whose target is not unique, such as low-light enhancement. We adapt one Qwen-Image-Edit model to six tasks with a single adapter trained in about three hours on one GPU. The image instruction outperforms text conditioning under a matched comparison, and it supports task agnostic restoration without a degradation label, which the text variant does not.
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All authors Süleyman Aslan, Görkay Aydemir, M{\i}sra Yavuz, Yunus Bilge Kurt, Nasrin Rahimi, Ahmet Rasim Emirdağ{\i}, Burak Can Biner, M. Ak{\i}n Y{\i}lmaz
Image and Video Processing
Entropy-map SSIM analysis of Salt and Pepper Noise Removal via Recursive Median Filterring
Petr Boriskov and Andrei Velichko
This paper studies the removal of salt-and-pepper (SP) noise from grayscale images using a median filter (MF) within a recursive thresholding
algorithm. Denoising performance is assessed using two complementary metrics: SSIM-Img and SSIM-Map.SSIM-Img is standard Image Quality Assessment (IQA), the conventional Structural Similarity Index (SSIM) computed between the restored and clean images. SSIM-Map is novel IQA, an SSIM-based evaluation computed between
entropy maps of these images, where the maps are obtained using singular value-decomposition
entropy in sliding windows. We show that SSIM-Map is more sensitive to residual impulse artifacts, blur, and local intensity transitions, and therefore complements the conventional SSIM-Img metric.
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Soft Condensed Matter
Universal behavior in diffuse charge dynamics with patterned electrodes
E. Krucker-Velasquez, E. Kirkinis and M. Olvera de la Cruz
Iontronic devices that replicate physiological processes will improve energy efficiency to support artificial-intelligence architectures. Energy storage and conversion devices, however, display disordered interfacial and bulk configurations that conventional impedance spectroscopy cannot resolve. Here, we show that the onset of frequency dispersion in the impedance of an electrolytic
cell with non-uniformly charged electrodes takes place at approximately twice the resonator natural frequency, independently of whether the
cell is driven by oscillating charges or voltage differences, and remains valid for both overlapping and non-overlapping double layers. In addition to characterizing energy storage devices, these universal findings apply to devices interfaced with biomaterials.
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Robotics
Cosserat Modeling of Trimmed Helicoid Soft Arms with a Separated-Section Constitutive Law
Zhihang Qin, Linxin Hou, Zeyu Zhong et al. (+10 more)
Cosserat rod models for soft
robots usually construct sectional stiffness by summing material properties over a common cross-section. This assumption becomes inaccurate for trimmed helicoid arms, where load-bearing helix domains are separated and connected only through sparse fused crossings. This paper formulates a separated-section constitutive law that evaluates each helix domain in its local frame and pulls its constitutive response back to the backbone, yielding an effective backbone stiffness. Sparse-fusion mechanics captures the additional compliance caused by relative motion between neighboring domains and determines channel-wise reduction profiles $\eta_c(s/L)$ for bending, torsion, and extension. The resulting effective sectional stiffness is strongly anisotropic: bending and extension are reduced by about one order of magnitude, whereas torsion remains close to the effective backbone stiffness. The resulting sectional law is embedded in a geometrically exact dynamic Cosserat model with GVS discretization and routed-tendon actuation. Across 103 measured configurations, the three
datasets give pooled normalized position errors of $7.7 \ \%$, $6.7 \ \%$, and $7.8 \ \%$, while each full-arm solve requires approximately $0.3 \ \mathrm{s}$ on one CPU core (Intel Xeon, Cascade Lake, $2.8 \mathrm{GHz}$), enabling rapid model-based planning, state and load estimation, and morphology–control co-design for architected soft
robots.
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All authors Zhihang Qin, Linxin Hou, Zeyu Zhong, Yuchen Sun, Wenci Xin, Yueheng Zhang, Ji Qi, Jie Wang, Peiyi Wang, Muhammad Sunny Nazeer, Yu Jun Tan, Federico Renda, Cecilia Laschi
Statistical Mechanics
Order-parameter-dependent mobility in the convective-viscous Cahn-Hilliard equation
P. O. Mchedlov-Petrosyan, L. N. Davydov and O. A. Osmayev
Generally the mobility in the Cahn-Hilliard equation may depend on the order parameter; this was evident already from the original derivation. However, explicit introduction of such dependence results in tremendous calculation difficulties, so the constant mobility approximation was commonly used. On the other hand, in several cases this dependence appeared to be crucial, so more realistic expressions were applied, usually positive powers or
polynomials; still, for such dependencies only approximate and/or numerical solution exist. We consider 'reciprocal' linear and quadratic dependencies of mobility on the order parameter; for these dependencies exact traveling wave solutions are obtained. Even more, in a limited interval of the parameters reciprocal quadratic mobilities are rather good approximations to
polynomial mobilities.
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Quantum Physics
A Quantum Phase-based Comparator
Alessandro Berti and Alessandro Poggiali
Quantum comparators decide the order of two operands. They rely on reversible logic acting on basis-encoded integers, so their width grows with the precision. We introduce the
Quantum Phase-based Comparator (QPC), which compares two values carried in relative phases instead. The
circuit places the two phases, entered with opposite signs, between a pair of Hadamard gates, and a fixed offset centers the interference, so that the probability of measuring zero falls below one half exactly when the first phase is the smaller. We give two implementations. With hard-coded values, the two values are written into the parameters of two phase gates, and the comparison costs one
qubit and five gates. With the register-driven cascade, the values are drawn from two $t$-
qubit registers through binary-weighted controlled-phase gates, at a cost of one
qubit beyond the registers and depth linear in $t$; since the cascade induces each phase linearly from the register content, one
circuit handles a superposition of operand pairs. The readout yields a biased coin rather than a definite bit, and we quantify the shots that a decision takes at a given phase separation. Scaling both phases by an integer widens the decision margin at no cost in width or depth, and an adaptive doubling schedule turns this amplification into a shot count logarithmic in the inverse separation.
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Software Engineering
TRAIL: Towards Effective Log Parsing for Rare Logs through Template-Preserving Augmentation
Sepideh Hodaeian, Zhenhao Li and An Ran Chen
Log parsing is essential for system log analysis because it supports tasks such as debugging, monitoring, and anomaly detection by transforming unstructured log messages into structured log templates. However, real-world log
datasets exhibit highly imbalanced, long-tailed distributions, where a small number of frequent templates dominate while many rare templates appear only a few times. This imbalance causes evaluation results to be overly optimistic by allowing frequent templates to dominate
benchmark metrics, while poor performance on rare yet operationally important events remains largely hidden. In this paper, we investigate the prevalence and impact of rare log groups, defined as log groups with fewer than five instances. Our empirical study on the widely used Loghub-2.0
benchmark shows that rare log groups account for nearly 20% of all templates but less than 0.01% of log messages. Because they contain only a handful of instances, all evaluated parsers experience substantial performance degradation on these groups. To address this challenge, we propose TAILOR, a log parsing framework that improves template inference for rare log groups through template-preserving augmentation. TAILOR enriches rare log groups with template- consistent log messages before template inference. The additional structural evidence helps distinguish static
tokens from dynamic variables. Our experimental results show that TAILOR improves parsing accuracy on rare log groups by 19% over the strongest baseline while maintaining competitive performance on complete
datasets. We further show that the proposed augmentation strategy generalizes across different LLM backbones and consistently improves existing LLM-based parsers without modifying their core architectures.
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High Energy Astrophysical Phenomena
A Magnetic Unification of Merging Supermassive Black Hole Binaries
Mark J. Avara, David O'Neill and Zoltán Haiman
The detection of an electromagnetic (EM) transient from a supermassive black hole binary (SMBHB) merging within a gaseous environment will constitute a multi-messenger milestone. Numerical simulations of these systems have mostly been performed in 2D with a constant viscosity parameter representing turbulent and
magnetic stresses. These simulations predict a steep drop in
accretion rate and X-ray luminosity at merger, followed by a slow recovery. However, 3D-GRMHD and 3D-MHD simulations have shown only a modest reduction in
accretion rate and a fast post-merger recovery. We present a new 2D pseudo-Newtonian framework with physically motivated prescriptions for
stochastic viscosity, relativistic apsidal precession, and large-scale
magnetic fields. When suitably calibrated, this 2D framework accurately captures key 3D-GRMHD behavior, demonstrating that it can be used to explore binary
accretion without the high computational cost of 3D-GRMHD simulations. We find that the merger signatures are most sensitive to the strength of the
magnetic field, with a drop in the
accretion rate at merger progressively less pronounced for stronger fields. Post-merger
evolution is determined by both magnetization and relativistic precession of orbiting gas, yielding rapid recovery of
accretion. For low-magnetization disks, recovery is driven by self-intersection shocks, and the dip at merger is followed by a distinct second dimming. Large-scale
magnetic fields of increasing magnitude lead to magnetically arrested disk (MAD) behavior which disturbs the merging system progressively earlier before merger, and longer after. Our results unify the EM signatures of the merger of embedded coplanar SMBHBs, and provide novel observational constraints on their intrinsic magnetization.
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Astrophysics of Galaxies
Giant Low-Surface Brightness Galaxies in the Global Picture of Galaxy Formation and Evolution
Fedor M. Kolganov, Igor V. Chilingarian, Anna S. Saburova et al. (+5 more)
Giant low-surface brightness
galaxies (gLSBGs) challenge standard merger-driven
galaxy assembly models due to their large, dynamically cold disks, with radii often larger than 100~kpc and dynamical masses exceeding ${\sim}10^{12}$~\Ms. We identify $60$ gLSBGs, including a volume-complete subsample of $35$
galaxies out to $z=0.1$ in the 120~sq.deg. area. Using new and archival photometric and spectroscopic data, we homogeneously analyze their structural properties and derive central velocity dispersions. We update the gLSBG volume density estimate in the local Universe to $(3.6 \pm 0.6) \cdot 10^{-5}~\text{Mpc}^{-3}$. This number corresponds to about 1 in 4000
galaxies in the $g$-band luminosity range $0.14-1.37 \cdot 10^{11}~L_{\odot}$ out to $z=0.1$, which is consistent with EAGLE cosmological simulations but 3.8 times lower than the corresponding value in TNG100. Central surface brightness and scalelength of the gLSB disks scale with the luminosity of the central component, becoming fainter and flatter as the central component grows more luminous, suggesting rapid disk formation rather than the inside-out growth typical of high surface brightness late-type
galaxies. Many gLSBGs also show extended UV counterparts in GALEX, strengthening their connection to XUV-disk
galaxies. Finally, comparison with superluminous spirals shows that even complete gas-to-
star conversion cannot transform a typical gLSBG into a superspiral, owing to their lower total baryonic mass.
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All authors Fedor M. Kolganov, Igor V. Chilingarian, Anna S. Saburova, Damir Gasymov, Evgenii V. Rubtsov, Vladislav I. Klochkov, Kirill A. Grishin, Mariia Demianenko
General Relativity and Quantum Cosmology
Kerr-Degenerate Shadows and Distinct Strong-Deflection Lensing in Rotating Hayward-like and Bardeen-like Geometries
Chen-Hung Hsiao, Limei Yuan and Yidun Wan
We study and compare rotating Hayward-like and Bardeen-like geometries within a common Kerr-like axisymmetric ansatz, with the models distinguished by their areal-radius functions $R_i(\rho)$. On the adopted radial branches, the positive minimum areal radius excludes the Kerr ring locus, and the
curvature invariants examined here remain finite. We identify the parameter domains containing two, one, or no horizons. The two geometries have the same asymptotic Komar charges but different finite-radius values. If we treat the Einstein tensor as an effective stress tensor, then for every nonzero regularity length $\ell$, this ansatz violates the null and weak energy conditions, as demonstrated by the equatorial radial-null contraction. For the complete
photon family on a black-hole branch, the shadow boundary is identical to that of Kerr at the same mass,
spin, and observer inclination, and is also identical for the two regular geometries. The corresponding area-equivalent shadow diameters for M87* and Sgr~A* are consistent with the approximate Event Horizon
Telescope (EHT)-based intervals adopted here, and we do not obtain further constraints on $\ell$. By contrast, the prograde equatorial strong-deflection limit (SDL) calculations are only partly degenerate. At fixed
spin, $u_m^+$ and $\theta_\infty^+$ are common to Kerr and the two regular geometries, whereas $\bar a_+$, $\bar b_+$, the image separation, the relative magnitude, and the subleading time-delay correction depend on $\ell$ and on the areal-radius profile. Within the stated strong-deflection approximation, future measurements of $\Delta T_{2,1}$, $s^+$, and $r_{\rm mag}^+$ may help test these rotating geometries and distinguish the Hayward-like and Bardeen-like predictions from Kerr, including in the one-horizon parameter domain where the adopted branch contains no Kerr-like inner horizon.
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Systems and Control
Higher-Order Approximation of Exit Functionals in Sampling-Based Stochastic Model Predictive Control
Sashank Modali and Takashi Tanaka
Safety evaluation in sampling-based
stochastic model predictive control often requires numerical estimation of exit functionals. The approximation of first-exit times and exit indicators is therefore a key numerical bottleneck, and discretization error in these quantities directly affects the resulting controller. This paper studies how existing higher-order methods for strong approximation of exit times can be brought into safe control. Two cases are highlighted. For general noncommutative dynamics, an adaptive order-1 Milstein discretization is used together with Lévy-area simulation via Wiktorsson's method. For commutative dynamics, an adaptive order-1.5 construction achieves a stronger exit-time rate. Under a local anti-concentration condition on the exit-time law, we show that strong exit-time approximation transfers to strong approximation of the failure indicator. The methods are then studied in the context of chance-constrained path integral control, which provides an exact continuous-time representation of safety through exit events. Numerical experiments compare the two cases in terms of strong exit-time error, failure-indicator error, and closed-loop constraint satisfaction, showing improvement over Euler-Maruyama and thereby enabling existing and future techniques whose applicability depends on improved strong approximation.
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Cryptography and Security
Partition-Matched Evaluation of Community Features under Distribution Shift in Android Malware Function-Call Graphs
Junru Zhu, Yixin Yang, Xiaoqing Ding et al. (+1 more)
Graph-based Android malware classifiers can lose accuracy under malware-type or family shifts. We test whether mesoscopic organization in function-call graphs provides shift-stable information beyond local degree profiles (LDP), global statistics, lightweight metadata, and size-matched random partitions. Using 15,000 MalNet-Tiny, Common, and Distinct graphs, six Leiden descriptors specified before evaluation, and five optimizer seeds, communities raise Tiny macro F1 from 78.7% to 81.3% but yield 29.4-point source-only Common degradation. One size-matched random partition yields 27.8-point degradation. Across five random partitions, mean degradation is 27.9 points; the 95% two-level bootstrap interval for random minus community degradation is [-4.2, 1.2] points. With metadata, the corresponding difference is -0.2 points with interval [-1.1, 0.6]. Removing modularity raises structure-only Common macro F1 from 51.9% to 53.6%. The tested signature adds IID signal but shows no repeatable shift-stability advantage, demonstrating why mesoscopic graph claims need partition-matched controls and repeated null draws.
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All authors Junru Zhu, Yixin Yang, Xiaoqing Ding, Ruoyu Qi
Dynamical Systems
Ergodic Averages over Shrinking Sectors of $\mathbb Z[i]$
Alex Burgin and Christina Giannitsi
We study ergodic averages over Gaussian integers in shrinking angular sectors, which impose an increasingly restrictive directional relation between the real and imaginary coordinates. In uniquely ergodic systems, averages of continuous functions along $\Omega$ converge to the invariant integral uniformly in the base point and sector location for sectors whose widths shrink subpolynomially in the norm cutoff. For finitely generated multiplicative actions, we prove a quantitative sector–disk comparison in an explicit logarithmic shrinking range, with a power-saving error in $\log N$, uniformly in the base point and sector location. The exponents depend only on the number of distinct
prime transformations, and the result requires neither ergodicity nor angular-distribution hypotheses on the sets of
primes inducing the individual transformations. Strong unique ergodicity then yields convergence to the invariant integral at each fixed base point. Consequences include asymptotic independence of relative angular position and $T^{\Omega(n)} x$, Gaussian Liouville cancellation, and joint equidistribution modulo integers of
prime-factor counts associated with two
prime classes having divergent reciprocal-norm sums.
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Artificial Intelligence
The AI Neuroscientist: An Interactive Agentic Interface for Neuroimaging Analysis
Aakash Patel, Panos Ketonis, Shreya Saxena et al. (+2 more)
Analyzing neuroimaging data requires specialized coding and statistical expertise, which limits accessibility for researchers without computational backgrounds. We present the AI Neuroscientist, a
language agent for interactive data exploration. The system integrates a large
language model (LLM) with a neuroimaging toolset to perform quality control, modeling, and visualization. This allows researchers to query data quality and specify analysis parameters directly in natural
language, providing a transparent and interactive alternative to conventional scripted pipelines for small-scale data exploration. We demonstrate these capabilities using functional near-infrared spectroscopy (fNIRS) data, and evaluate the
agent on a custom fNIRS benchmarking suite against general-purpose LLM
agents with code sandboxes. Future extensions will generalize the architecture to additional modalities, including functional
magnetic resonance imaging (fMRI) data, and expand the benchmarking suite to additional fNIRS tasks.
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All authors Aakash Patel, Panos Ketonis, Shreya Saxena, Smita Krishnaswamy, David van Dijk
High Energy Physics (Theory)
Primary Decompositions in Lorentz-Covariant Rings
Giuseppe De Laurentis and David Tai
An effective strategy for constructing compact representations of integral coefficients in scattering amplitudes is to make their singularity structure manifest. This is naturally achieved by treating them as elements of fraction fields of
polynomial quotient rings, where poles correspond to algebraic vanishing loci. Simplification via partial fraction decomposition relies on exploiting the structure of the numerators and is governed, at least in a first approximation, by their behaviour on irreducible codimension-two varieties. These are identifiable through primary decompositions of the associated ideals. Despite the availability of general
algorithms, primary decompositions remain computationally challenging and often require tailored strategies. In this work, we extend an approach based on ideal saturation, identify an issue related to the breaking of Lorentz covariance in the choice of ideal generators, and present a solution based on fitting ansätze in special kinematic limits. We also provide substantial improvements to an
algorithm for the generation of numerical points in proximity of given varieties and to one for testing primality of unmixed ideals, both of which rely on a semi-numerical procedure for computing independent sets and variety dimensions. Finally, we present new codimension-two primary decompositions for five-point massless amplitudes at higher powers in the dimensional regulator, and for finite remainders with five-point one-mass kinematics, where a new spurious singular locus appears.
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Statistical Mechanics
Approach to Steady-State in Nested Resetting Processes
Callum Britton, Ben Le Jeune, Henry Alston et al. (+2 more)
We characterise the approach to nonequilibrium steady state in nested resetting processes by deriving accumulation times for the system, which quantify the effective first-passage time for the local establishment of steady state. For equal resetting rates, we obtain closed-form expressions showing that relaxation propagates as a wavefront with a delay that increases linearly along the resetting chain. We then extend this analysis to heterogeneous resetting rates, deriving exact steady-state distributions, spatial moments and accumulation times for both degenerate and non-degenerate resetting rates. We show that relaxation to steady state is ultimately governed by the minimum resetting rate in the system and, in degenerate systems, its multiplicity. These results provide a comprehensive analytical description of first-passage to nonequilibrium steady state in hierarchically coupled
stochastic resetting systems, with potential applications to relaxation and transport processes in biological systems.
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All authors Callum Britton, Ben Le Jeune, Henry Alston, Paul C. Bressloff, Thibault Bertrand
Quantum Physics
Exploring Asymmetric QEC Code Concatenation
Sayam Sethi, Maxwell Poster, Aditi Awasthi et al. (+3 more)
Concatenated
quantum error-correcting codes have recently gained popularity because they improve the effective distance of a joint code without requiring the discovery of new codes with desirable parameters. A common building block for such studies is the $[[4,1,2]]$ Iceberg code, the smallest error-detecting code and therefore the one with the lowest resource overhead. However, it has an asymmetric number of $X$ and $Z$ checks, and concatenating it produces skewed logical error rates (LERs) between the $X$ and $Z$ observables. This asymmetry can be mitigated by Clifford-deforming the individual blocks of the concatenation, but the space of possible Hadamard deformations grows doubly exponentially with the number of concatenation levels. We formalize this deformation space and investigate it by proposing six concrete deformation strategies, which we compare under a code capacity (with and without bias) noise model using a concatenated maximum-likelihood soft-information decoder. Among the strategies we study, one one of them achieves near-identical $X$ and $Z$ LERs in a logical-
memory experiment while simultaneously reducing the total LER (the sum of the $X$ and $Z$ error rates).
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All authors Sayam Sethi, Maxwell Poster, Aditi Awasthi, Willers Yang, Joshua Viszlai, Jonathan Mark Baker
Differential Geometry
A gluing construction of $Z$-extremal Kähler metrics and new examples of Bismut Hermitian-Einstein $3$-folds
Vestislav Apostolov, Abdellah Lahdili, Kuan-Hui Lee et al. (+1 more)
We present a gluing construction for a class of 6th order
geometric non-linear PDE's on a Kähler
manifold or orbifold, arising in the study of Bismut Hermitian Einstein complex $3$-folds, the supersymmetric supergravity models in physics, and in Dervan's Z-critical Kähler metrics. As applications, we obtain a desingularization
theorem for orbifolds with crepant singularities admitting a solution of the PDE, as well as a blowing-up of points
theorem for smooth complex $3$-folds admitting such solutions. These results yield new examples of regular Bismut Hermitian Einstein structures on the smooth
manifolds $3(S^2\times S^4)\sharp 4(S^3\times S^3)$, $5(S^2\times S^4)\sharp 6(S^3\times S^3)$ and $S^1\times 9(S^2\times S^3)$, as well as infinite topological types of smooth compact $7$-dimensional
manifolds $Y_7$, providing new solutions to the supersymmetric ${\rm AdS}_3 \times Y_7$ type IIB and ${\rm AdS}_2 \times T^2 \times Y^7$ type $D=11$ supergravity models.
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All authors Vestislav Apostolov, Abdellah Lahdili, Kuan-Hui Lee, Frédéric Rochon
High Energy Physics (Theory)
Anyon Crystallization by Statistics
Zohar Komargodski, Xuzixiang Lou, Ivri Nagar et al. (+3 more)
We revisit the long-standing problem of the anyon gas. We study the solvable near-bosonic limit of a large number of anyons $N$ at fixed $N/k$, where $1/k$ characterizes the statistical interaction. In this limit, the ground state is a
crystal with order $N/k$
lattice sites. The statistical interactions, mediated by a long-range gauge field, are completely screened within the crystalline phase. The resulting state is a conventional
crystal without superfluidity. We characterize this anyon
crystal by determining its unit-
cell size, ground-state energy, acoustic excitations, angular momentum crossings, and other properties. Our results indicate a
quantum phase transition between the
crystal at $k>k_c$ and a superfluid at $k<k_c$, with a critical value $k_c\simeq2\text{--}5$. These results imply that an anyon gas with only statistical interactions is not necessarily a superfluid, contrary to common lore. We also present a detailed analysis of the renormalization-group
evolution of anyon–anyon interactions, identifying many new potential fixed points and elucidating finite-size effects.
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All authors Zohar Komargodski, Xuzixiang Lou, Ivri Nagar, Domenico Orlando, Susanne Reffert, Amit Sever
Quantum Physics
In-orbit operation of a programmable quantum photonic processor
Simon Steiner, Peter Schiansky, Antonius Scherer et al. (+17 more)
Quantum technologies promise computational capabilities beyond the reach of classical systems. A forward-looking application lies in satellite missions, which increasingly depend on onboard computing under stringent constraints on size, weight and power.
Quantum photonics is particularly attractive here:
photon interference can enhance the machine-learning models needed to process large onboard data volumes, at fixed hardware resources. However, harnessing this interference requires more than generating single
photons, as they must remain mutually indistinguishable: a fragile condition that is hard to maintain within the technically demanding framework of a space mission, which includes a rocket launch, strong thermal drifts, and radiation. This is why
quantum states of light, though already generated and transmitted in orbit for secure communication and fundamental tests, have never been used as a computational resource. Here, we report a programmable
quantum photonic platform operating on a nanosatellite, processing two
photons in a six-mode universal integrated
circuit. By programming distinct
unitaries and tuning the
photons into indistinguishability, we observe two-
photon interference, establishing the on-board generation, manipulation and detection of non-classical light. This extends space-based technologies towards in-orbit
quantum-assisted computing, for instance local encoding of Earth-observation data, or nodes in a distributed
quantum network.
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All authors Simon Steiner, Peter Schiansky, Antonius Scherer, Riccardo Albiero, Mathias Dragosits, Zhenghao Yin, Martin Mauser, Patrik Zahálka, Raphael Pimenta, Cristóbal Melo, Cédric Léonard, Abhiram Rajan, Niki Di Giano, Antonino Caime, Roberto Osellame, Francesco Ceccarelli, Daniel Martínez, Tobias Guggemos, Iris Agresti, Philip Walther
Computer Vision
Geometric and Semantic Coupling for Interaction Understanding in 3D Scenes
Hanyang Kong and Xingyi Yang
Understanding interaction in a 3D scene requires recovering movable parts, their motion, and where they can be operated. These quantities are related, and their predictions can inform one another. A closed cabinet door, for instance, reveals a movable surface but may leave the hinge side ambiguous; its handle helps resolve this ambiguity, while the part provides context for localizing and interpreting the small handle. Building on this observation, we present SEGMENT-SNAP, which combines
geometric and semantic evidence through part-handle coupling. Three independently trained predictors recover movable parts, dense handles, and part-associated handle proposals. We couple their outputs in two directions. For part motion, a training-free
geometric decoder fits predicted part surfaces under explicit physical priors and uses detected handles to select candidate hinge lines. For handle prediction, a part-conditioned branch proposes additional handles, while standalone part classes refine their rotation/translation labels, with dense-handle labels as a fallback. Each transfer is applied once, without iterative feedback. On the Articulate3D validation set, handle guidance raises motion-gated AP from 13.74 to 40.98 under fixed masks and axes. Additional handle proposals raise handle AP from 24.63 to 29.65, and full contextual class correction raises it to 30.99 in the reference configuration. Fixed-input controls, retraining ablations, learned-decoder comparisons, and paired visualizations together characterize the benefits and limits of this coupling. Our system also achieved first place in the Articulate3D Challenge.
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Quantum Physics
Shreya Shukla, Abhijith Jayakumar and Andrey Y. Lokhov
Learning
quantum Hamiltonians from low-temperature thermal state measurements is a fundamental problem in
quantum physics. Scalability of existing methods is limited by the complexity of semidefinite optimization problems or partition function computation. Here, we develop a
quantum analog of classical score matching method that exploits generalized notions of derivatives and integration by parts in operator
algebras. We introduce a new Operator Score Matching loss function that recovers
Hamiltonian parameters by a simple gradient descent without the need to compute intractable normalization constants. Numerical experiments on the Gibbs states of the transverse-field Ising model, XXZ
spin chain,
Fermi-Hubbard model, and
lattice $\phi^4$ theory demonstrate efficient parameter recovery using finite-depth truncations of nested commutator expansions. Our results establish Operator Score Matching as a practical, partition-function-free framework for
quantum Hamiltonian learning.
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Quantum Physics
A dual-region ytterbium tweezer apparatus with an in-vacuum nanofiber cavity
Shunichiro Hashimoto, Hideki Ozawa, Yusuke Hisai et al. (+2 more)
We present a dual-region experimental apparatus for trapping single atoms of the fermionic isotope ytterbium-171 (${}^{171}\mathrm{Yb}$) with optical tweezers in a science chamber containing an in-
vacuum nanofiber cavity. A source region is located approximately $240\,\mathrm{mm}$ from the science chamber, preserving optical access around the chamber for high-numerical-aperture tweezer optics. A pulsed transport sequence based on longitudinal moving molasses launches atomic pulses toward the science chamber, where they are decelerated and recaptured in a magneto-optical trap. We subsequently load single atoms into a $2\times2$ tweezer array located approximately $50\,\mathrm{\mu m}$ from the nanofiber and measure a $1/e$ lifetime of $9.5(7)\,\mathrm{s}$. These results demonstrate that the dual-region architecture provides sufficient ${}^{171}\mathrm{Yb}$ atoms for single-atom tweezer operation near an in-
vacuum nanofiber cavity.
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All authors Shunichiro Hashimoto, Hideki Ozawa, Yusuke Hisai, Takao Aoki, Ryotaro Inoue
Human-Computer Interaction
How Children Design and Reason about Trustworthy AI Chatbots
Deniz Ozturk, Jiayu Li, Daksh Pratap Singh et al. (+9 more)
Children increasingly interact with AI chatbots, making trust calibration essential to AI literacy. Prior research has examined children's trust in AI mainly as users evaluating systems built by others, rather than as designers of their own chatbots. We developed a chatbot-building environment with adjustable trust-relevant traits (e.g., confidence, transparency, formality, assertiveness), rules, and persona. We conducted mixed-methods study with 115 learners (ages 8-18) who made 119 chatbots. We examined how children configured their chatbots, reasoned about trustworthiness, and how closely chatbot behavior aligned with their designs. Younger students (age 10-13) set significantly higher confidence than older students (age 14-18), and some deliberately built chatbots that gave wrong answers on purpose, yet still called them trustworthy, arguing that a chatbot does what it was built to do. Younger students equated trust with purpose-fulfillment, while older students linked it to transparent, calibrated design. Students also calibrated academic chatbots to be more transparent and formal than hobby chatbots. We identify seven design dimensions describing what children believe makes a chatbot trustworthy, and discuss implications for AI literacy tools.
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All authors Deniz Ozturk, Jiayu Li, Daksh Pratap Singh, Yasitha Rajapaksha, Fasika Melese, Bahare Riahi, Shiyan Jiang, Qiao Jin, Joey Huang, Veronica Cateté, Tiffany Barnes, Xiaoyi Tian