Human-Computer Interaction
ChartRevive: Reconstructing Data Visualizations from Chart Images Using MLLM
Yuki Ueno and Aditeya Pandey
Static chart images are widely used in scientific publications, business reports, and presentations, yet recovering both the underlying data and visual design from chart images remains a labor-intensive manual process, making them difficult to reuse. While prior work has primarily focused on data extraction, the extraction of visual design specifications, including colors, marker shapes, and axis configurations, remains underexplored. To identify a suitable model for chart reconstruction, we systematically
benchmark five
multimodal large
language models (MLLMs) across five basic chart types on both data and design extraction tasks. Our evaluation shows that textual and categorical information can generally be extracted reliably, whereas numeric and spatial information remain challenging. Among the evaluated models, GPT-5.4 achieves the best overall performance and is adopted as the backbone of our system. Guided by these findings, we present ChartRevive, a mixed-initiative system that combines MLLM-based extraction with an interactive verification interface, supporting users to efficiently inspect, correct, and refine reconstructed charts through overlay-based verification and real-time rebuilding.
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Robotics
Planning Trajectories that Bounce: Reflection Classes for Collision-Tolerant Robots
Subhadeep Koley, Subhrajit Bhattacharya and David Saldaña
Robot navigation methods tend to avoid contact, and consequently search for collision-free trajectories. For
robots with high inertia and limited maneuverability, however, avoiding contact can require substantial steering effort and time, even when interactions with surrounding surfaces could be safely exploited. In this paper, we develop a planning method that deliberately uses controlled wall reflections to generate trajectories that can be easier and more efficient to execute than purely collision-free motion. We consider planar
navigation in environments where a mobile
robot is permitted to bounce off surrounding surfaces. To represent the resulting alternatives, we construct a reflection-augmented state graph in which paths are partitioned into distinct classes according to the sequence of walls used for reflection. This representation enables systematic enumeration of reflection strategies and identification of the lowest-cost path within each class. We show that, although a reflecting path cannot be shorter than the shortest collision-free path, it can reduce execution time and actuation effort by replacing costly changes in heading with controlled environmental interactions. The planned trajectories are executed using a contact-aware sampling-based controller with the
robot's full dynamics. In our experiments, we demonstrate that in our simulated test scenario, the best reflecting class can reduce time and control effort. Our results show that controlled contact can provide dynamically advantageous
navigation strategies that are excluded by conventional collision-avoidance formulations.
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Machine Learning
Learning Risk Scores Robust to Unobserved Confounders
Ryan Edmonds, Yingxiao Ye, Sina Aghaei et al. (+3 more)
We consider the problem of learning risk scores to prioritize individuals for scarce resources or interventions, from historical observational data affected by unobserved confounding. Decisions about who receives scarce resources are often guided by risk scores based on recorded characteristics, such as responses to a survey. These risk scores are increasingly being learned directly from observational data: historical records of individuals' characteristics, allocation decisions, and outcomes. Standard methods such as inverse propensity weighting (IPW), which corrects for the bias introduced by the historical allocation policy, can be used to learn accurate risk scores if the historical decision process is fully explained by the recorded characteristics. In practice, however, historical decisions often depend on unrecorded information, causing learned risk scores to systematically under-prioritize exactly the individuals whose unrecorded circumstances drove past prioritization. We propose a method for learning risk scores that are robust to this kind of unobserved confounding, building on IPW. Since propensity weights cannot be reliably estimated under unobserved confounding, we instead treat them as belonging to an uncertainty set determined by the observable data and domain-informed estimates of the degree of confounding, combining sensitivity analysis from causal inference with Wasserstein distributionally robust optimization. The resulting robust risk score learning problem admits a sample-based approximation that we reformulate as an exponential cone program compatible with off-the-shelf solvers. We demonstrate the effectiveness of our approach on semi-synthetic data derived from
datasets in the UCI Machine Learning Repository. Our method improves calibration by up to 29.2% over traditional
benchmarks and up to 11.1% over the state of the art, without compromising other metrics.
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All authors Ryan Edmonds, Yingxiao Ye, Sina Aghaei, Andrés Gómez, Çağ{\i}l Koçyiğit, Phebe Vayanos
Computer Vision
A Systematic Evaluation of Infrastructure-Based Radar System for Highway Traffic Monitoring
Tianheng Zhu, Woei-chyi Chang, Alamss Riaz et al. (+2 more)
Infrastructure-based radar systems offer robust and long-range solutions for traffic monitoring, yet their detection and tracking performance under real-world conditions remains insufficiently evaluated. This study introduces DRaT (Drone and Radar Trajectories), a dual-modality
dataset of naturalistic vehicle trajectories collected at a highway merging segment in Fort Worth, Texas, to systematically assess radar sensing performance against drone-derived ground truth. The performance is evaluated at three levels: individual vehicle detection, trajectory tracking, and macroscopic traffic parameter estimation. For individual vehicle detection, the radar achieves an overall precision of 78% and a recall of 57%, with degraded performance under congested traffic conditions and at longer distances. At the trajectory level, the radar demonstrates reasonably strong tracking performance (IDF1 = 0.699), maintaining reliable vehicle identities when tracks are successfully established. For macroscopic traffic flow metrics, the radar accurately estimates space-mean speed (MAPE < 4%) but underestimates density and volume by approximately 23% due to missed detections. The paper also discusses practical deployment considerations and potential downstream applications of roadside radar sensing systems. To support reproducible research on infrastructure-based sensing systems, we have open-sourced the DRaT
dataset on Zenodo: https://zenodo.org/records/20171110.
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All authors Tianheng Zhu, Woei-chyi Chang, Alamss Riaz, Sogand Hasanzadeh, Yiheng Feng
Computer Vision
MINER: Multi-crop INference-time Enhancement for Rare-Object Retrieval with Frozen Dual Encoders
Abdulmalik Alquwayfili, Faisal AlMeshal, Jumanah Almajnouni et al. (+2 more)
Text-to-image
retrieval with frozen dual encoders degrades when the query names a small, visually subordinate object in a cluttered scene: a single global image
embedding underrepresents the localized visual evidence. We present MINER, a training-free inference framework that augments a frozen dual encoder's global image
embedding with a small bank of region-level
embeddings and a hubness-correcting similarity rescoring, recovering visual evidence that global pooling underweights. To evaluate this setting, we introduce ROCS, a
benchmark built from high-clutter subsets of Flickr30K and MS COCO whose images are re-captioned to name a single low-salience object. Experiments on CLIP, SigLIP, and SigLIP 2 show that MINER improves
retrieval on every backbone, on ROCS and on the standard splits. Analyses show that these gains come primarily from broader spatial coverage rather than precise crop placement, revealing a simple and general way to recover localized evidence from frozen representations. Code: https://github.com/aalquwayfili/MINER.
Dataset: https://huggingface.co/
datasets/aalquwayfili/ROCS.
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All authors Abdulmalik Alquwayfili, Faisal AlMeshal, Jumanah Almajnouni, Huda Abdulhadi Alamri, Muhammad Kamran J Khan
Physics and Society
Percolation in the Stochastic Block Model
Luca Pieleanu, Moritz Laber, Narayan G. Sabhahit et al. (+1 more)
The
stochastic block model is a paradigmatic model of networks with community structure. Yet percolation in the model has been studied primarily in cases with a fixed block structure, even though in real networks, the community structure may evolve as the network grows. Here we study percolation in sequences of
stochastic block models in which the numbers and sizes of communities, as well as the intra- and intercommunity connection probabilities may all change with the network size. We analyze five such sequences using two methods: linearized self-consistent equations for the locally tree-like models and a branching process at the community scale for the models with nonvanishing clustering. We find that the critical average degree is not generally equal to $1$, even in locally tree-like sequences, because the transition depends on how connections are distributed across the evolving community structure. We also show that the community-scale branching process accurately predicts the transition when intercommunity connections are sufficiently sparse, even in the presence of nonvanishing clustering, while a
geometric stochastic block model sequence demonstrates the limitations of this method when correlations between intercommunity connections cannot be neglected. These results extend percolation studies in the
stochastic block model to more realistic scenarios with evolving community structure, and may provide new methods to derive the upper and lower bounds for the percolation threshold in
geometric long-range percolation.
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All authors Luca Pieleanu, Moritz Laber, Narayan G. Sabhahit, Dmitri Krioukov
Instrumentation and Methods for Astrophysics
Increasing Sensitivity to Trailed Solar System Objects in Archival JWST NIRCam Imaging with Group Differencing
Anthony Girmenia and Stanimir Metchev
The JWST archive contains thousands of NIRCam exposures distributed across the sky. Small
solar system bodies (SSSBs) are present in many of these fields, but are often trailed and more difficult to detect. The standard pipeline produces a single image by fitting a slope to a sequence of $N$ cumulative “up-the-ramp" groups of the detector. Thus, each JWST image comprises a time series of cumulative reads. We demonstrate that transforming the ramp into a series of group difference images reduces the per-image trail length of moving sources, producing an $(N-1)$-group photometrically calibrated time series from an $N$-group integration. This enables higher-cadence trajectory-based searches using as few as a single JWST exposure. A generalized least-squares fit of the group difference images along the trajectory of a moving source recovers the source flux at higher SNR when compared to the ramp fit, with the improvement scaling with the reduction in trailing. We validate our procedure on 13 moving objects detected in archival JWST NIRCam images. To estimate our sensitivity increase, we perform an injection/recovery simulation on the JWST imagery. Trailed moving objects recovered via group differencing show higher SNRs, improving completeness to moving sources compared to standard calibrations. For typical integration lengths of $N=4$ or $6$ for archival JWST/NIRCam exposures in the ecliptic, group differencing yields sensitivity gains between $\sim 1.1$–$1.4$ mag, based on simulation results. This method significantly increases the sensitivity of archival JWST surveys to trailed SSSBs.
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Computer Vision
A Hierarchy-Aware Video-Language Model Evaluation and Hyperbolic Baseline for Surgery
Ana Manzano Rodríguez, Pascal Mettes, Marlies P. Schijven et al. (+1 more)
Surgical procedures follow a phase-to-step hierarchy, yet the
video-
language models used to recognize them are evaluated with flat per-level metrics that ignore cross-level coherence and error structure. In this paper we make two contributions to address this problem, (i) we introduce SurgHiBench, the first hierarchy-aware evaluation suite for
surgical video understanding, with three tasks measuring recognition, consistency, and severity across granularity levels. We evaluate a general-purpose CLIP model, a Euclidean
surgical model, and, as second contribution: (ii) HyperSurg, a new
hyperbolic model that enforces phase-step containment via entailment cones, across four (existing)
datasets spanning three procedure types. The suite reveals that two models with the same accuracy can produce predictions of very different error severity, ranging from sibling confusions within the correct phase to unrelated cross-phase predictions.
Hyperbolic geometry shifts predictions toward the correct procedural neighborhood, and these gains scale with the tree-likeness of each
dataset's annotation hierarchy, providing a principled indicator when hierarchy-aware geometry helps.
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All authors Ana Manzano Rodríguez, Pascal Mettes, Marlies P. Schijven, Cees G. M. Snoek
Mathematical Finance
Surface-Driven Stochastic Volatility for Commodity Options: Identification of Stochastic Vol-of-Vol and Leverage from Smile Dynamics
Arthur Steve Tchoneteck, Tingjia Zhang and Frederi Viens
Commodity option surfaces contain information beyond the at-the-money
volatility level. We develop a surface-driven
stochastic-
volatility framework for soybean futures options using daily Chicago Mercantile Exchange Group
Volatility Index (CME CVOL) indicators from October 2013 to August 2025. The ATM level and convexity are positive, right-skewed, and well described by log Ornstein Uhlenbeck dynamics, whereas the additive skew changes sign and is modeled on the real line by an Ornstein Uhlenbeck process. These empirical features motivate a joint mean-reverting surface-factor system. Under a log OU futures
volatility model, leading-order smile relations map the ATM level to the latent
volatility state, convexity to an effective surface-implied
volatility- of-
volatility state, and skew to an effective leverage state. We derive the associated risk-neutral pricing equation and Feynman Kac representation,
benchmark prices by Monte Carlo simulation, validate the pricing PDE by finite differences, and train a neural-network surrogate for fast repeated valuation. The empirical results show substantial variation in surface states across
market regimes, with the 2021 La Nina drought window exhibiting elevated
volatility and skewness. The numerical results show close agreement between finite-difference and Monte Carlo prices over the validation grid, while the neural-network surrogate reproduces the pricing map at substantially lower inference cost. Overall, the results support the use of level, skew, and convexity jointly as dynamic inputs for commodity-option pricing and model diagnostics.
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Fluid Dynamics
Blade secondary vortex interaction noise in hovering rotors
Jordon Won and Seongkyu Lee
Recent indoor experiments on small-scale hovering rotors have reported mid-frequency quasi-tonal peaks of uncertain origin in the residual (aperiodic) far-field sound spectrum, even without wake recirculation. This study investigates their physical origin using hybrid Reynolds-averaged Navier-Stokes/large-eddy simulations coupled with a Ffowcs Williams-Hawkings acoustic analogy, applied to a four-bladed ideally twisted rotor in hover. The high-resolution simulation reproduces the measured higher-harmonic blade-passage-frequency tones, confirming that they arise from the rotor flow itself and not from the enclosed-chamber test environment. Flow-field analysis attributes the tones to blade secondary
vortex interaction (BSVI): the outboard blade sections, between approximately r/R = 0.88 and 0.96, are repeatedly impinged upon by coherent, S-shaped secondary
vortex worms formed by entrainment of the wake shear layer into the primary tip
vortices of preceding blades. These aperiodic secondary
vortices produce blade-to-blade correlated loading, yielding a quasi-tonal signature distinct from both purely periodic blade-
vortex interaction and mostly
stochastic blade-wake interaction noise. Spectral proper orthogonal decomposition of the zero-mean upwash confirms that the mid-frequency (3-10 kHz) content is dominated by a single, low-rank, spatially coherent mode associated with the braids, whereas a lower-frequency mode near 1 kHz is linked to the primary tip
vortex core. BSVI is thus identified as a distinct and previously uncharacterized source of aperiodic tonal noise in hovering rotors, with direct implications for the acoustic design of small- to medium-scale rotorcraft and electric vertical take-off and landing propulsors.
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Differential Geometry
Topological Obstructions to Riemannian-Lorentzian Continuation
Nathalie E. Rieger and Ödül Tetik
Motivated by problems in instanton continuation, we establish geometro-topological selection rules for continuing a Riemannian region into a Lorentzian region through a smooth transverse change of signature. Given a collar-gluing $M=\overline{M_R} \cup_{\mathcal{H}}\overline{M_L}$ of a smooth
manifold $M$ along a hypersurface $\mathcal{H}$, we show that an admissible signature-changing metric of the form $\widetilde g=g-fV^\flat\otimes V^\flat$ with $g$ a Riemannian metric, having Riemannian signature on $M_R$ and Lorentzian signature on $M_L$, exists if and only if a relative Euler class obstruction on $\overline{M_L}$ vanishes. This obstruction combines ordinary Euler characteristics $\chi$ in a manner that depends on which components of $\mathcal{H}$ represent big bangs and which represent big crunches. It also restricts the choice of $M_R$; for example, if $M$ is closed and $\dim M$ is even, then it requires$\chi(\overline{M_R})=\chi(M)$. We also determine and classify the possible compactness types of $\overline{M_R}$, $\mathcal{H}$, and $\overline{M_L}$ occurring in such a collar-gluing construction, and construct explicit signature-changing metrics in each case.
Topology change can be entirely contained within the Riemannian region when $M$ is the interior of a compact
manifold with boundary.
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Statistical Methodology
Bayesian Nonparametric Approaches to Ordinal Drought Modeling in the United States
Mostafa Shams, Robert Erhardt and Staci Hepler
Data observed over space and time can exhibit dependence across both dimensions, and
datasets can grow very large in both the number of locations and the number of time periods. This dependence and data size mean that many statistical models cannot be fit at a reasonable computational cost as a result of dense
matrix inversions, large parameter spaces, or
memory and storage challenges. When the data are ordinal, this only adds to the computational complexity of model fitting. Many ordinal models rely on a latent continuous variable designed to capture dependence in a computationally efficient manner, and then partitioned according to cutoff parameters to yield the observed ordinal response data. A very common choice is a latent Gaussian distribution, which can accommodate different dependence structures and permits Gibbs sampling in a
Bayesian framework. Unfortunately, this model can be overly restrictive and fails to provide the flexibility needed to capture a range of ordinal outcomes. In this paper, we demonstrate the use of
Bayesian nonparametric (BNP) methods to enhance the model flexibility of a Gaussian latent model for ordinal drought data observed over space and time, with Dirichlet process priors inducing clustering among time periods within each spatial location. In this work, we model ordinal drought data separately at each spatial location while accounting for temporal dependence, but we do not model spatial dependence across locations. We show that these BNP models often outperform
Bayesian parametric approaches at a reasonable computational cost.
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Theoretical Economics
Rare States and Long-Run Pricing
Ye Lu and John Stachurski
Macroeconomic crises are rare, yet they can have large effects on asset prices. Because the probability of entering a crisis from normal times is small relative to the probability of recovery, the dynamics are nearly reducible: setting the crisis-entry probability to zero makes crisis states unreachable from normal states. We develop a graph-theoretic and spectral analysis of long-run pricing under reducible and nearly reducible dynamics. We show that, at the reducible limit, physical and pricing dynamics induce the same classes of economic states and accessibility relations, but rank their long-run importance differently: by recurrence and by class-specific pricing rates. Moreover, a claim's price depends only on its pricing corridor—the classes of states lying on directed paths from the initial state to states where the payoff is positive—and the highest class-specific pricing rate within this corridor determines the claim's long-run pricing rate. When the reducible-limit corridor excludes the globally pricing-dominant class, we characterize how restoring rare transitions introduces a contribution from that class with a weight that vanishes as the transitions become rarer. This contribution becomes dominant only beyond a crossover maturity, which increases with the rarity of the connecting paths and decreases with the dominant class's pricing-rate advantage. Finally, a consumption-based disaster-recovery application shows how preferences, consumption risk, cash-flow exposure, and recovery timing jointly shape the long-run pricing dominance of disaster states and crossover maturities.
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Functional Analysis
Reflectionless measures with singular continuous components on Widom sets
Long Li
We give a detailed implementation of the mass-splitting construction of Nazarov, Volberg and Yuditskii for reflectionless measures with singular continuous components. The finite approximants are allowed to have isolated atoms. An exact local operation replaces each active atom by a short permanent interval and two child atoms, thereby eliminating the dependence of a future splitting scale on an already prescribed band width. Summable estimates for completed generations and fixed cylinders retain positive mass on a null Cantor set and exclude atoms in the limit. Uniform analytic estimates near the permanent intervals establish reflectionlessness and exclude endpoint singularities. A separate Green function estimate for thin pairs of intervals gives the Widom condition and regularity of the limiting compact set. The resulting probability measure has both a nonzero absolutely continuous component and a nonzero singular continuous component.
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Strongly Correlated Electrons
Exact Equivariance from Ordinary Neural Networks for Lattice Many-Body Dynamics
Ho Jang, Sankha Subhra Bakshi and Gia-Wei Chern
Large-scale simulations of correlated electron systems require repeated evaluations of the electronic forces and transition energies driving collective dynamics. Machine-learning surrogates alleviate this bottleneck, but incorporating
symmetry often involves carefully designed descriptors or specialized network architectures. We show that ordinary multilayer perceptrons equipped with finite-group averaging provide exactly equivariant surrogates directly from microscopic configurations. The construction separates
symmetry enforcement from the internal network architecture and applies to both discrete and continuous
lattice degrees of freedom. In the Falicov–Kimball model, it predicts directional hopping free-energy differences; in the Holstein model, its invariant limit generates conservative
lattice forces.
Benchmarks against exact diagonalization establish microscopic accuracy and agreement of dynamical correlations, while large-scale simulations recover multiscale charge ordering and charge-density-wave coarsening. These results demonstrate an accessible, reusable route to
symmetry-preserving many-body dynamics for systems with finite
lattice point groups.
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Computer Vision
Super-Resolution of Solar Magnetograms via Adaptive Stratified Ensemble Learning with Uncertainty Estimation
Sina Norouzi Kandalan, Haodi Jiang, Jason T. L. Wang et al. (+1 more)
Single-image super-resolution of Sun's photospheric magnetograms enables consistent analysis across heterogeneous space-based instruments and supports long-term studies of
solar magnetic field
evolution. We address the super-resolution task from SOHO/MDI (low-resolution) to SDO/HMI (high-resolution) line-of-sight (LOS) magnetograms using a modified RRDBNet architecture initialized by ESRGAN pretrained weights. Through systematic per-image diagnostic analysis, we identify image complexity as the dominant predictor of reconstruction errors. To exploit this finding, we introduce an adaptive stratified specialist ensemble (SSE) of three specialist networks with uncertainty estimation, where each specialist network is trained by images from three different complexity strata using a weighted random sampling strategy. During inference, a lightweight router based on input image statistics assigns each test image to the appropriate specialist network. Our experimental results demonstrate the good performance of the proposed ensemble and its superiority over closely related methods.
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All authors Sina Norouzi Kandalan, Haodi Jiang, Jason T. L. Wang, Qin Li
Astrophysics of Galaxies
Search for High-Ionization Nebular Emission (SHINE). I. A Systematically Selected [Ne V] Sample at z > 3 with JWST/NIRSpec PRISM
Shobita Satyapal, Sara Doan, Daniel Schaerer et al. (+8 more)
We conduct the first systematic Search for High-Ionization Nebular Emission (SHINE) using [Ne V]$\lambda3426$ in JWST/NIRSpec PRISM spectroscopy at $z>3$. From more than 9,000
galaxies, we identify 25 [Ne V] emitters spanning $z=3.059$-$9.444$ and $\log_{10}(L_{\rm [Ne\,V]}/erg\,s^{-1})=40.70$-$42.86$. Their [Ne V] luminosities overlap with those of local [Ne V]-selected active
galactic nuclei (AGNs) and exceed those of local metal-poor [Ne V] emitters, although such low-luminosity systems would fall below our sensitivity. The population is diverse, spanning compact and extended morphologies and a broad range of continuum properties and
stellar masses, including very low-mass hosts. It includes sources with broad Balmer emission as well as others whose higher-resolution spectra do not require a broad component. The [Ne V] emitters overlap only partly with conventional AGN diagnostics: most do not satisfy conservative high-
redshift AGN criteria based on strong rest-optical narrow-line ratios, and only a minority have adopted broad-line classifications. [Ne V] upper limits for independently selected broad-line AGNs and little red dots are too shallow to establish a population-wide [Ne V] deficit. Six sources have secure Chandra counterparts, showing that X-ray weakness is not universal among luminous [Ne V] emitters. Strong [Ne V]/[Ne III] emission is associated with redder ultraviolet slopes and stronger Balmer breaks, but not with UV luminosity, possibly linking strong high-ionization emission to recent changes in
star formation. The observed incidence of luminous [Ne V] emission shows no significant
evolution over $z>3$, despite an increasing robust [Ne III] detection fraction. The high [Ne V] luminosities and hard line ratios favor black-hole
accretion as the dominant power source, establishing [Ne V] emission as a complementary probe of early black-hole growth.
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All authors Shobita Satyapal, Sara Doan, Daniel Schaerer, Rui Marques-Chaves, Anurag Sawarkar, William Matzko, Camilo Vazquez, C. Daoutis, D. Korber, I. Morel, Mengyuan Xia
Quantum Physics
Quantum Soundness of a Total-Degree Line-versus-Point Test
Honghao Fu, Nengkun Yu and Tianrun Zhao
We prove
quantum soundness of the total-degree diagonal line-vs-point test using the individual-degree soundness
theorem of Ji, Natarajan, Vidick, Wright, and Yuen. A random change of coordinates yields projective
polynomial decoders of total degree at most $md$. The uniform-line slice of the test bounds the weight of outcomes of degree greater than $d$, which are removed by a common relabeling. This reduction does not yield a dimension-independent soundness bound: the $\operatorname{poly}(m)$ dependence of the individual-degree
theorem persists, as discussed in Section 1.2 of arXiv:2009.12982.
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Quantum Physics
Efficient Learning of Clifford-Scrambled Product States
Tobias Haug
Clifford
circuits acting on product magic states provide a compact ansatz exhibiting extensive magic, volume-law
entanglement, and even classically hard sampling under standard complexity assumptions. Here, we efficiently recover its hidden product subsystems from two-copy Bell sampling. Quadratic relations between Bell samples determine the irreducible blocks after removing Pauli stabilizers, and binary linear
algebra constructs a Clifford disentangler. For logarithmic-size blocks, approximate learning of the full state is efficient whenever the state remains inverse-polynomially separated from acquiring additional Pauli stabilizers. We efficiently learn $n$-
qubit states prepared by random $T$-doped Clifford
circuits with $T$-gate density below one via $O(n^2)$ Bell samples, and disentangle hidden product blocks in Clifford-augmented
matrix product states. For
unitary learning, we exactly learn $T$-depth-one
circuits using $O(n^2)$ queries. Finally, we rule out pseudorandom states and
unitaries with a product bipartition hidden by Clifford
circuits.
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High Energy Astrophysical Phenomena
EP260119a: A High-Redshift Gamma-Ray Quiet Fast X-ray Transient Probing a Potentially Hidden Population of Relativistic Explosions
Camila Angulo-Valdez, Asuka Kuwata, Edilberto Aguilar-Ruiz et al. (+41 more)
Gamma-ray quiet fast X-ray transients (FXTs) provide a new approach for studying relativistic explosions that evade traditional gamma-ray triggers. In this work, we present multi-wavelength observations and analysis of EP260119a, a high-$z$ FXT detected by Einstein Probe/WXT and followed up with COLIBRÍ, the Liverpool
Telescope, and other facilities. Spectroscopy yields a
redshift of $z = 5.47$, making EP260119a the most distant FXT detected by Einstein Probe to date. Despite its luminous X-ray and optical emission, no
prompt gamma-ray counterpart was detected by SVOM/ECLAIRs, SVOM/GRM, Swift/BAT, or Konus/Wind, despite contemporaneous coverage. The broadband afterglow is well described by synchrotron emission from a uniform relativistic jet propagating into a shallowly stratified external medium close to the constant-density limit, indicating a standard relativistic explosion despite the absence of detectable gamma rays. Occupying the extreme high-$z$ end of the growing Einstein Probe FXT sample, EP260119a supports the possibility that soft X-ray surveys are uncovering relativistic transients that remain undetected by current gamma-ray instruments. Our results indicate that some explosions in the early Universe escape conventional gamma-ray surveys and demonstrate the value of combining sensitive soft X-ray discovery with rapid optical follow-up to obtain a more complete view of the high-$z$ transient population.
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All authors Camila Angulo-Valdez, Asuka Kuwata, Edilberto Aguilar-Ruiz, Rosa L. Becerra, Ramandeep Gill, Miguel Angel Aloy, Antonio de Ugarte Postigo, Alan M. Watson, Chen-Wei Wang, Diego López-Cámara, Daniel A. Perley, Aleksandra Bochenek, Simone Dichiara, Enrique Moreno Méndez, Nathaniel R. Butler, Benjamin Schneider, Jean-Luc Atteia, Stéphane Basa, William H. Lee, Dalya Akl, Sarah Antier, Alexis Coleiro, James DeLaunay, Damien Dornic, Jean-Grégoire Ducoin, Francis Fortin, Dmitry Frederiks, Leonardo García-García, Noémie Globus, Olivier Godet, Jamie Kennea, Gianluca Lombardi, Alexandra L. Lysenko, Francesco Magnani, Tyler Parsotan, Margarita Pereyra, Anna Ridnaia, Samuele Ronchini, Fredd Sánchez-Álvarez, Dmitry Svinkin, Anastasia Tsvetkova, Mikhail Ulanov, Peter Veres, Hui Yang
Astrophysics of Galaxies
Sequential Star-Formation and the Near-Infrared Stellar Populations within N159
Nolan Habel, E. Sabbi, Peter Zeidler et al. (+19 more)
We present the first results from a JWST/NIRCam study of the
star-forming region N159, one of the richest H II complexes in the Large Magellanic Cloud. This region stretches over $\sim$130 pc, and exemplifies large-scale sequential
star formation. We present photometric results of $>$300,000 point sources combining data from ten near-infrared JWST filters spanning 1-4.7 microns. We detect sources as faint as $\sim$27 magnitudes at F115W, corresponding to
stellar masses reaching below $\sim$0.1 $M_{\odot}$. By constructing several color-magnitude and color-color diagrams, we separate young
stars from main-sequence and older
stars within N159, finding evidence for
stellar populations younger than 0.5 Myr. Of the young population, we identify accreting, pre-main sequence
stars from their Paschen-$\alpha$ (Pa$\alpha$) excess and a population of sources bearing strong infrared (IR) excess, representing candidate young
stellar objects (YSOs). Within this population, we identify a subpopulation of sources with H$_2$O ice absorption. The IR-excess-bearing YSO candidates correlate spatially with
dust filaments and molecular gas demonstrating their embedded state. The distribution of
stellar and candidate YSO populations across N159 reveal more evolved
star-forming populations in the N159E region and newer
star formation in the N159W region, supporting the sequential
star formation scenario for this region.
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All authors Nolan Habel, E. Sabbi, Peter Zeidler, Tuila Ziliotto, Laurie Chu Margaret Meixner, Guido De Marchi, O. C. Jones, Alec S. Hirschauer, Beena Meena, Jeroen Jaspers, Laura Lenkic, Remy Indebetouw, Karl D. Gordon, Burcu Gunay, Suzanne Madden, James Muzerolle Page, Omnarayani Nayak, Antonella Nota, Theo Oneill, Massimo Robberto, Tony Wong, Petia Yanchulova Mercia-Jones
Dynamical Systems
Twisted Patterson-Sullivan densities on nilpotent covers for Anosov subgroups
Krishnendu Gongopadhyay and Neelanjan Mondal
Let $\Gamma<\mathsf{SL}(d,\mathbb R)$ be a torsion-free, Zariski-dense Borel-Anosov subgroup, and let $\varphi$ be a linear functional positive on the limit cone of $\Gamma$. For every $\chi \in \operatorname{Hom}(\Gamma, \mathbb R)$, we construct twisted $(\varphi,\chi)$-conformal densities and prove their existence, uniqueness and ergodicity. For every non-trivial normal subgroup $\Gamma_0\lhd\Gamma$ with nilpotent quotient, we classify the ergodic $\varphi$-conformal densities of $\Gamma_0$ in terms of characters of $\Gamma/\Gamma_0$. Up to scaling, they are naturally parametrized by $\operatorname{Hom}(\Gamma/\Gamma_0,\mathbb R)$, and their dimensions are precisely the critical exponents of the corresponding twisted Poincaré series. We also prove a shadow lemma for twisted densities of normal subgroups, with estimates uniform over densities with fixed parameters, and a twisted
Hopf-Tsuji-Sullivan
theorem.
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Cryptography and Security
When Clients Are Orchestrated: Strategic Gradient Manipulation to Defeat Federated Learning Servers with Efficient Defense
Mohamed Shaaban, Ahmed Abdelnaby and Mohamed Elmahallawy
Federated Learning enables decentralized model training by exchanging model updates–rather than raw data–with a central parameter server (PS). While most of the existing defenses primarily assume static or independently acting adversaries, we reveal a new class of dynamically adaptive attacks that systematically bypass such protections. We propose Fed-ADR, a holistic attack framework in which a malicious orchestrator server (OS) dynamically coordinates a heterogeneous set of
adversarial clients, including both targeted and untargeted attackers. Through real-time coordination by the OS, malicious clients strategically adapt their gradient updates to evade defenses deployed by the PS, while either severely degrading global model performance or steering training toward
adversarial objectives.To mitigate this threat, we offer a detection mechanism that estimates each client's true gradient from historical updates, enabling real-time detection of coordinated malicious behavior without additional overhead. We further introduce an in-situ recovery mechanism that restores global model performance without restarting training, preserving convergence and minimizing recovery time. Comprehensive experiments on MNIST, Fashion-MNIST, and CIFAR-10
benchmark datasets demonstrate that Fed-ADR's attack scheme can reduce global accuracy from over 90% to below 10%, bypassing several state-of-the-art defenses. When our detection and recovery modules are employed, they identify malicious clients and restore accuracy to over 90% within a few rounds, at a substantially lower cost than retraining from scratch–achieving a reduction of at least 20x in computational overhead.
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Computer Vision
PEARL: A Lightweight Prompt-based Feature Interpreter Framework for Real-Time, Anonymous, and Heterogeneous Collaborative Perception
Armin Maleki and Hayder Radha
Heterogeneity across Collaborative Perception (CP)
agents is a major challenge for emerging CP frameworks due to domain gaps from differing sensors, architectures, and training data. Prior works mitigate this challenge by aligning features in a unified space via model retraining or per-
agent-type interpreters. These strategies (a) require access to neighbor configurations, (b) do not fully address real-time CP deployment, and (c) generalize poorly to unseen
agents joining at run time. To overcome these challenges, we present PEARL, a
Prompt-
Embedding framework for Anonymous and Real-time Lightweight heterogeneous CP. PEARL supports multiple CP interpreters and selects one for a new-joining
agent in real time using two lightweight, multi-scale interpreters trained in parallel: a sparse-detection (LWSD) interpreter that aligns salient regions for cooperative detection, and a dense, domain-invariant (LWDDI) interpreter that produces
agent-invariant features for fast interpreter selection. Both interpreters use low-rank visual
prompts to reduce computation, storage, and model complexity. Extensive experiments on simulated (OPV2V, V2XSet) and real (DAIR-V2X)
datasets show that PEARL generalizes across simulated and real-world cooperative driving scenarios. Its real-time model-selection strategy yields an 8.2% Average Precision (AP) gain over a random-selection baseline while running in 1.67 ms on average. Although primarily designed for real-time CP, PEARL also outperforms state-of-the-art heterogeneous CP frameworks under traditional offline training by 5.6% AP on average while reducing communication cost by up to 34.7 times. Equally important, PEARL does not require sharing
agents' configurations or model settings, thereby protecting information that may be proprietary or private. These results establish PEARL as a scalable and practical framework for heterogeneous collaborative perception.
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Probability
Domains of Attraction for Quasi-Stationary Distributions of Branching Processes in Random Environments
Pei-Sen Li
We obtain a complete characterization of the quasi-stationary distributions of subcritical stable continuous-state branching processes in Brownian random environments, together with the domain of attraction of each member. These distributions form a one-parameter family whose modified Laplace transforms are given explicitly in terms of Tricomi's confluent hypergeometric function. For every initial distribution, we determine whether the conditional law converges to a QSD and identify that QSD whenever it does. The necessary and sufficient criteria are formulated in terms of the tail probabilities and moments of the initial law.
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Astrophysics of Galaxies
A High Resolution View of Three Massive Young Stellar Objects in the Magellanic Clouds
Yu Cheng, Kei I. Tanaka, Takashi Shimonishi et al. (+4 more)
The Magellanic Clouds provide an excellent laboratory to study massive
star formation beyond typical
Galactic environments. Using ALMA band 7 long-baseline observations, we present the highest resolution (approximately 0.03", $1500{\rm\:au}$) view to date of three massive young
stellar objects (MYSOs) in the Magellanic Clouds, i.e., Lh03 and Lh09 in the LMC, and S07 in the SMC. The 0.87 mm continuum, with a sensitivity of $\sim30\:\mu$Jy beam$^{-1}$, reveals varying levels of fragmentation, from a single compact source (S07), a few (Lh03), to a cluster of compact sources (Lh09). In Lh09, we identified 17 core candidates with lowest measured mass of 2.6 Msun, and their de-projected separations peak around 6000~au. All three targets exhibit high velocity CO(3–2)
outflows, including detection of a collimated bipolar jet driven by a core in Lh09. Spectral lines from HCO$^+$, SO, and SO$_2$ are detected in all sources. We observe velocity gradients that likely trace rotating toroids (Lh03) or more complex motions (Lh09, S07). Taken together, these phenomena observed on $\sim1500$~au scales are qualitatively similar to those seen in
Galactic MYSOs, suggesting a degree of universality in the basic processes of massive
star formation across different environments. Nevertheless, the substantial source-to-source diversity in continuum morphology and dense-gas kinematics motivates larger high-resolution surveys to determine how these properties vary with source characteristics and environment.
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All authors Yu Cheng, Kei I. Tanaka, Takashi Shimonishi, Yichen Zhang, Suinan Zhang, Kenji Furuya, Asako Sato
Quantum Physics
Dual Boundary Condition Inference: A Two-Boundary Product Rule and Its Implications
Luis Razo and Eliahu Cohen
Many inference problems are constrained from two sides: forward information and a second constraint on outcomes. Dual Boundary Condition Inference (DBCI) multiplies them and renormalises. The rule is not new: it is the product-of-experts form and the unit-exponent member of the logarithmic-pooling family. The paper contributes a classification and reducibility characterisation. The
algebra does not fix the exponent at which the inputs combine: given the statistic log p_fwd + log p_bwd, which already builds in the product form, maximum
entropy supplies the family (p_fwd p_bwd)^lambda and selects no member. If the inputs are read as two equally weighted opinions, three requirements select lambda = 1/2: unanimity preservation, consistency under a common
Bayesian update, and minimal symmetric Kullback-Leibler divergence. If they are read as separately applied factors, two requirements select lambda = 1: a neutral second input must leave the first unchanged, and an update to one input must pass through unchanged. For a rank-one intermediate projective measurement, the Aharonov-Bergmann-Lebowitz (ABL) rule realises this form with no extra parameter. Every positive exponent orders outcomes identically, so the choice is invisible to picking the likeliest outcome, but not to scoring a class by total mass. Under the relevant identifications, the same product form gives Bayes' rule, while a final effect proportional to the identity returns the ordinary forward Born probabilities. The second result is a Two-Boundary Reducibility Criterion: DBCI factors through the forward boundary exactly when the effective backward boundary, up to positive rescaling, does. So a fixed prior or model-fixed structural constraint adds no distinctions between cases sharing the same forward boundary, though it may encode substantial information and still materially affect the result.
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Astrophysics of Galaxies
New Optical and Mid-Infrared RR Lyrae Period-Luminosity-Metallicity Relations for Precision Distances and Metallicity Distributions in the Magellanic Clouds
Clara E. Martínez-Vázquez, Joseph P. Mullen, Massimo Marengo et al. (+11 more)
RR Lyrae (RRL)
stars, thanks to the existence of their period-luminosity relations, have been widely used as primary standard candles within the Milky Way and the Local Group of
galaxies. We present in this work the latest empirical RRL period-luminosity-
metallicity (PLZ) and period-Wesenheit-
metallicity (PWZ) relations combining optical ($V, I$) and mid-infrared data ($W1$) using OGLE and WISE photometry, anchored to
Gaia DR3 parallaxes. We calibrate these relations with the largest, most complete, and homogeneous catalog of
Galactic halo field RRL spectroscopic
metallicities. These new PLZ/PWZ relations provide distance measurements in remarkable agreement with those derived from eclipsing binaries in the Large and Small Magellanic Clouds (MC): $(m-M)_{0, LMC} = 18.47 \pm 0.07 (stat) \pm 0.05 (sys)$ mag and $(m-M)_{0, SMC} = 18.87 \pm 0.09 (stat) \pm 0.06 (sys)$ mag (within 1% and 10% of eclipsing binary distances, respectively). We have also re-calibrated photometric
metallicity relations to derive individual [Fe/H] abundances of LMC and SMC RRL
stars. This allowed us to investigate the
metallicity gradient of the MCs and discover two distinct populations of fundamental mode RRL
stars in the LMC (only one is instead found in the SMC). Our newly derived mid-infrared period-luminosity relations are poised to become an essential tool to derive precise distances of older
stellar populations in the Local Group of
galaxies, and can easily be adapted to observations obtained in JWST NIRCam photometric bands.
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All authors Clara E. Martínez-Vázquez, Joseph P. Mullen, Massimo Marengo, Giuseppe Bono, Vittorio F. Braga, Santi Cassisi, Brian Chaboyer, Massimo Dall'Ora, Valentina D'Orazi, Michele Fabrizio, Giuliana Fiorentino, Matteo Monelli, Peter B. Stetson, Frédéric Thévenin
Computer Vision
From greenhouse climate to individual leaves: an organ-resolved model of lettuce growth
Md Hasibur Rahman, Faraz Ahmed, Hafiz Muhammad Bilal et al. (+3 more)
Greenhouse
climate management aims to improve crop production while limiting energy use. This requires knowing how a crop will respond before conditions are changed. A crop digital twin can support this decision only if it represents how plant physiology and structure develop together. A unified framework was developed to simulate lettuce growth from the physiology of individual leaves. Each leaf received the conditions at its position in the canopy and contributed carbon through photosynthesis. Part of this carbon was used for maintenance and the remainder supported growth, distributed among leaves by their age, size and local environment. The predicted leaf mass, area and age generated an evolving three-dimensional plant in NVIDIA Isaac Sim. Ray tracing calculated the radiation intercepted by each leaf and returned it to photosynthesis, so structure and growth influenced each other over time. Against greenhouse measurements, the relative root mean square error was 9.5% for total dry weight and 9.2%, 12.7% and 13.1% for leaf number, canopy diameter and largest-leaf area, respectively. A 30% decrease in incident radiation reduced final dry weight by 10.4%, while the same increase raised it by 6.9%, and adding 200 ppm carbon dioxide raised it by 46.1%. Within a simulated 40-plant block, interior plants accumulated 8.6% less dry weight than border plants with identical initial states, and the leaf-specific tipburn index rose in the enclosed leaves over the period in which tipburn appeared on the greenhouse plants. Resolving individual leaves therefore explains how local exposure changes plant growth within the greenhouse. The framework provides the forward plant model needed for a bidirectional digital twin, where observations of the physical plant can update predictions and support greenhouse
climate decisions.
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All authors Md Hasibur Rahman, Faraz Ahmed, Hafiz Muhammad Bilal, Daniel Wells, Dylan Tobin, Tanzeel U. Rehman
Data Structures and Algorithms
Minimum Sum Vertex Cover via Minimum Vertex Cover
Ahmad Biniaz, Jean-Lou De Carufel, Anil Maheshwari et al. (+2 more)
The Minimum Sum Vertex Cover (MSVC) problem asks for an ordering of the vertices of a graph that minimizes the sum, over all edges, of the time at which each edge is first covered. We study the problem through the structure of vertex covers and obtain new approximation and exact
algorithms, together with conditional lower bounds. For graphs of maximum degree $\Delta$, we show that a simple ordering
algorithm based on a minimum vertex cover achieves approximation ratio $R_\Delta \le {(\sqrt{\Delta}+1)}/{2}$. For $d$-regular graphs, we give a
polynomial-time $1.184$-approximation by combining Max-$k$-Vertex-Cover approximation with a structural bound on optimal prefixes. On the exact side, we give an
algorithm parameterized by the vertex cover number $k$ running in $2^{O(k\log k)} + O(n+m)$ time, improving the previous dependence on $k$, where $n$ and $m$ are the number of vertices and edges in the graph, respectively. We also develop a separator-based exact
algorithm running in $ 2^{O(\sqrt n \log n)}$ time on planar, bounded-genus, and fixed-minor-free graph classes. Finally, we prove that Minimum Sum Vertex Cover is NP-hard on planar graphs and, assuming ETH, admits no $2^{o(\sqrt n)}$-time exact
algorithm on $n$-vertex planar graphs. Thus our planar upper bound is tight up to logarithmic factors in the exponent.
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All authors Ahmad Biniaz, Jean-Lou De Carufel, Anil Maheshwari, Saeed Odak, Michiel Smid