Papers

The recent discovery of intrinsic superconductivity in stoichiometric FeTe films has renewed interest in the Te-rich end member of the iron chalcogenides for studies of unconventional and topological superconductivity, yet its intrinsic electronic structure remains unresolved. In this work, we combine molecular beam epitaxy, angle-resolved photoemission spectroscopy (ARPES), electrical transport measurements, density functional theory, and embedded dynamical mean-field theory to track the electronic reconstruction of 20-unit-cell FeTe films as Te annealing progressively removes excess interstitial Fe and drives the system from an antiferromagnetic metal to a superconductor. We find that this evolution is accompanied by recovered quasiparticle coherence, reduced electronic correlations, a Lifshitz transition, and a topological phase transition, yielding dxy-dominated hole and electron pockets that favor inter-pocket scattering. In addition, a shallow dxz/dyz-derived hole band located about 2 meV below the Fermi level may provide an incipient-band pairing channel, while scattering between the two electron pockets at M may offer additional pairing channels. High-resolution polarization-dependent laser ARPES measurements further reveal a topological surface state whose circular dichroism is consistent with the expected orbital-angular-momentum texture of stoichiometric FeTe. These results establish the intrinsic low-energy electronic structure of superconducting FeTe and identify the electronic states most relevant to superconductivity. The coexistence of intrinsic superconductivity and a topological surface state establishes stoichiometric FeTe as a promising platform for exploring topological superconductivity and possible Majorana bound states.

Proteins can possess numerous mutations relative to their wild-type amino acid sequences with minimal impact to their structure and function. However, in other cases, even a single amino acid mutation relative to the wild-type sequence can lead to a large change in structure or even a disease phenotype. While the accuracy of wild-type protein structure prediction has improved significantly in recent years, it remains difficult to accurately predict the structure of mutant proteins. Here, we characterize the local mutation-induced structural changes in proteins for a dataset of wildtype and the corresponding single-amino acid mutant x-ray crystal structures from the Protein Data Bank (PDB). We find that mutation-induced structural changes in these proteins are localized at the site of the mutation, decaying rapidly with increasing spatial distance from the mutation site. In addition, we evaluate how well AlphaFold3 can recapitulate the observed mutation-induced structural deformations in the x-ray crystal structures. We find that the accuracy of the AlphaFold3 predictions decreases strongly with increasing mutation-induced deformation. In contrast to the results for AlphaFold3, the Pearson correlation between a single physical feature, i.e. the change in solvent accessibility, and the mutation-induced deformation does not depend on the magnitude of the deformation. Our results and analyses provide a framework for further studies aimed at predicting the structural changes in proteins caused by single amino acid mutations.

Multi-robot systems have shown increasing viability in construction due to their ability to execute high-precision actions while reducing human exposure to hazardous tasks. However, these environments have high-dimensional configuration spaces and possess substantial collision-avoidance constraints, which include other robots, assembly objects, and workspace boundaries. We utilize a single factor graph for trajectory estimation and planning that incorporates measured robot states together with explicit collision and learned cable constraints. This supports changing workspaces and enables synchronized, high-dimensional robot motion planning while accounting for the stiff, vibration-induced uncertainty of heavy robotic systems. We demonstrate the success of our framework on the construction of a post-and-lintel structure using one robot arm as a timber gripper, and a second robot as a nail-fastener.

Diffusion models provide a flexible framework for motion generation, but turning this flexibility into closed-loop humanoid control remains challenging. Hierarchical generator-tracker systems steer motion through reference trajectories, yet these references may exceed the capabilities of the downstream tracker, leaving physical feasibility and disturbance recovery largely to a separate control module. Action-only diffusion avoids this separation by directly generating executable actions, but provides no explicit future-state trajectory that can be steered toward test-time motion objectives. Joint state-action diffusion offers a natural alternative, but existing controllers often rely on privileged full-body states, while learned behavior selection and test-time motion steering remain only partially integrated. We present PredActor, a predictive action diffusion policy that unifies both steering modes in one directly executed policy using proprioception alone. Given proprioceptive history and optional task context, PredActor jointly predicts actions and an internal future-state trajectory that enables guidance: classifier-free guidance strengthens text-conditioned motion, while classifier guidance steers future states toward test-time objectives. Only actions are executed, requiring neither a motion-reference tracker nor privileged full-body states. In simulation, PredActor reaches 44 of 45 destination targets and achieves a text retrieval score of 0.539 versus 0.424 for conditional action diffusion, with similar disturbance survival. Rolling denoising and computation-preserving runtime optimizations reduce the complete callback to 16.790 ms median and 19.383 ms p95 on a Jetson Orin NX, within the 20 ms control period. Deployed on a Unitree G1, PredActor demonstrates text-conditioned motion, disturbance response, joystick control, and semantic interpolation in simulation and hardware.

Feed-forward 3D reconstruction models enable efficient geometry estimation from sparse images, but their pretrained nature can make them vulnerable to distribution shifts beyond their training data. Identifying these failure modes is important for understanding when such models can be reliably deployed in unconstrained imaging settings. We investigate viewpoint variation as a controlled distribution shift by varying the angular span of sparse image inputs while keeping the input budget fixed. Across multiple feed-forward reconstruction models, we observe substantial degradation as viewpoint span increases, with wide spans producing both incomplete surface coverage and geometry unsupported by the observed imagery. These results reveal that viewpoint variation can induce failure modes beyond conventional reconstruction incompleteness, highlighting the need to evaluate pretrained feed-forward models under distribution shifts that challenge their learned geometric priors.

Medical agents increasingly combine general reasoning models with specialized clinical tools, yet their capabilities remain largely fixed by what clinicians and engineers design before deployment. Recursive self-improvement (RSI) offers a different paradigm in which agents learn from their own failures and autonomously expand their capabilities, but directly applying RSI to medicine introduces fundamental safety challenges. We introduce MedRSI, the first recursive self-improvement framework for medicine, which continuously transforms diagnostic failures into new clinical capabilities through tool composition and task-specific model training. Inspired by clinical practice, MedRSI introduces two mechanisms for clinically aligned self-evolution. Clinical-cost-aware failure prioritization directs improvement toward errors according to their potential clinical consequences rather than frequency alone. Fast discovery with slow registration separates rapid capability invention from conservative adoption, allowing new tools to enter the persistent agent only after demonstrating sustained benefit across subsequent patient cohorts. Across public glaucoma and heart disease benchmarks and two private clinical tasks, MedRSI progressively develops segmentation, measurement, prediction, multimodal reasoning, and generative capabilities, surpasses manually engineered medical agents, and autonomously discovers solutions to clinical problems not anticipated by its original designers. Our results show that medical agents need not remain constrained by capabilities specified before deployment: with clinically grounded mechanisms governing what to improve and what to retain, they can continuously construct, validate, and accumulate new capabilities from diagnostic experience. Code is available at https://github.com/ImprintLab/MedRSI.

We present three-dimensional general relativistic magnetohydrodynamic simulations of a precessing supermassive black hole binary with mass ratio q=1/7, embedded in a circumbinary disk. We investigate how dynamics within the binary cavity launch and shape a relativistic jet to thousands of gravitational radii. The primary black hole dominates both accretion and jet power, while the smaller black hole repeatedly crosses and shocks the inner accretion flow. These passages of the secondary drive magnetized gas alternately into the upper and lower jet funnels, producing disturbances that propagate outward as quasi-periodic outflows. Although the inner flow is perturbed twice per orbit, each jet lobe shows one dominant outflow per orbit. On longer timescales, the jet precesses, reverses its magnetic handedness, and undergoes two weak phases. The first results from a reorientation of the primary spin and is followed, after the jet recovers, by a reversal in the circulation of the projected magnetic field. The second weak phase occurs as the secondary's orbit becomes nearly coplanar with the circumbinary disk, reducing the gas and magnetic flux reaching the primary. Together, these results connect cavity dynamics to large-scale jet variability and predict a set of correlated jet signatures whose relative timing and causal ordering may provide stronger evidence for a small-mass-ratio supermassive black hole binary than any one feature alone.

In this paper, we study Zhang twist of Leavitt path algebra $L_K(E)$ using a graded automorphism $\sigma$ induced by a graph automorphism such that the twisted algebra is a Leavitt path algebra over another graph $E_{\sigma}$ which is a twisted graph obtained from the original graph $E$. We also show that the hypothesis that $\sigma$ be induced by a graph automorphism cannot be dropped as we construct a graded automorphism of the Leavitt path algebra of an infinite graph whose Zhang twist is not isomorphic to any Leavitt path algebra. We also define the notion of noncommutative projective scheme for $\mathbb Z$-graded algebras that coincides with the notion of noncommutative projective scheme for connected $\mathbb N$-graded algebras defined by Artin and Zhang and study it in the context of twists of Leavitt path algebras. We establish combinatorial connection between the graphs $E$ and $E_{\sigma}$ and as a consequence, we show that many ring-theoretic properties are invariant for Leavitt path algebras under the twist by $\sigma$. In particular, we show that the invariant basis number property is preserved under Zhang twists considered here for finite graphs, whereas the module type is not preserved.

Entanglement and Bell nonlocality have different sharing constraints across subsystems of a multipartite quantum system. We examine simultaneous violations of Bell inequalities with two-outcome observables and two or three measurement settings per party. We identify configurations whose simultaneous violations can be demonstrated on IBM Quantum hardware, and derive bounds and trade-off relations for several polygamous configurations. The results connect experimental capabilities of quantum computers with mathematical methods for studying the limits of quantum correlations.

We study the smooth stabilization number of a locally flat embedded surface in a smooth $4$-manifold, defined as the minimum number of connected sums with $S^2\times S^2$ needed to make the surface topologically isotopic to a smooth embedding. We prove that a nonsmoothable locally flat surface in a closed, simply connected smooth $4$-manifold becomes smoothable after a single stabilization if its homology class is primitive and noncharacteristic and its complement has abelian fundamental group. This yields infinitely many examples of nonsmoothable surfaces with smooth stabilization number one. We also exhibit examples for which a single stabilization produces infinitely many pairwise smoothly nonisotopic representatives in the same topological isotopy class. In contrast, we construct a locally flat genus-eight surface representing $6h$ in $\mathbb{CP}^2$ whose smooth stabilization number is exactly two. More generally, we show that smooth stabilization numbers of locally flat embedded surfaces in $\mathbb{CP}^2$ are unbounded.

The integration of AI into larger technical infrastructures has made the alignment of human trust with system trustworthiness, known as trust calibration, a critical engineering concern, since misplaced trust in either direction leads to operational and safety risks. While conceptual frameworks provide a strong foundation for understanding trust calibration, their translation into running systems remains a challenge, because there are few testbeds in which human trust inputs, machine trustworthiness evidence, gap detection and remediation operate together within a closed loop. This paper instantiates CRiDiT (Computational Risk-Sensitive biDirectional Trust) as a run-time testbed, operationalising machine-side trust with Dempster-Shafer Theory and PCR5 redistribution, human-side trust with Subjective Logic, and calibration with a threshold-based trust gap. Following the Design Science Research methodology, we exercise the artifact across three high-stakes scenarios (hiring, financial, legal), producing 144 logged interaction steps across fifteen sessions. The analysis shows that the artifact captures trust calibration dynamics as intended, and reveals three points at which the instantiated policy departs from its design requirements: the machine-side estimate begins from a global benchmark rather than task-relevant evidence; risk-sensitive thresholds do not produce risk-sensitive triggering; and the calibration policy assigns explanatory prompts to over-trust, where corrections narrowed the gap in all 6 observed cases. Since the first two arise from the same design decision, to make the difference of two estimated scalars the calibration criterion, they point toward a common requirement: that the criterion should operate on the evidence rather than on scalars derived from it. The third concerns what follows detection, and shows that the action vocabulary inherited from trust repair does not align with what the interaction logs show to be effective. The work contributes the artifact, a characterisation of its run-time behaviour, and the requirements this characterisation elicits.

This paper studies the minimum time trajectoriesvof a car-like mobile robot navigating in an obstacle free environment. The robot, with forward and backward speeds, is controlled by bounded front-wheels acceleration and limited front-wheels steering rate. The paper extends previous results which solved this problem for the kinematic car-like robot. However, the kinematic model assumes pure rolling at the wheels ground contacts. This assumption requires non-sliding constraints for the front and rear wheels that can only be handled by the robot dynamics. This paper formulates the non-sliding constraints based on the robot dynamics then augments the kinematic model time-optimal path primitives with three new path primitives associated with the non-sliding constraints. The three non-sliding path primitives together with the kinematic model twelve path primitives form the car-like robot time optimal trajectories. Approximate analytic solutions for the non-sliding path primitives are also provided. Examples study the time-optimal path primitives along representative maneuvers, illustrating how the non-sliding constraints influence the time optimal trajectories of the car-like robot.

Agents have attracted considerably increasing attention due to the power of executing both Reasoning and Acting (ReAct) in open and dynamic environments. The ReAct process typically exhibits a multi-turn trajectory in which one drives Large Language Models (LLMs) to generate both reasoning chains and task-specific actions in an interleaved manner. However, agents often suffer from significant uncertainty, where identical tasks yield divergent trajectories; trajectories with higher uncertainty often produce incomprehensible behaviors, severely undermining agent credibility. This work conjectures that such trajectory-level uncertainty frequently stems from cumulative turn-level reasoning uncertainty induced by LLMs; the latter often exhibits a collection of branches of divergent reasoning chains and their resulting actions. Built upon this, we present the Graph-based Reasoning UncErtainty in Trajectories (GRUET) method for the uncertainty quantification of ReAct, comprising turn-level reasoning uncertainty quantification and trajectory-level uncertainty aggregation; the former precisely quantifies reasoning uncertainty via modeling the reasoning space spanned by potential reasoning branches as a graph and then approximating the reasoning space complexity with graph complexity, while the latter employs simple aggregation strategies for quantifying the overall trajectory credibility. Empirical evaluations across nine LLMs and five benchmarks validate the effectiveness of our proposed GRUET in terms of selective generation performance, measured by AUROC, AUPRC, and AUARC.

We prove global well-posedness and scattering for the three-dimensional defocusing cubic nonlinear Schrödinger equation with arbitrary initial data in $H^s(\R^3)$, $s>\frac{1}{2}$, without radial symmetry. The proof combines the $I$-method with long-time $L^2_{t,x}$ bounds for products of frequency-localized components of the nonlinear solution. The key high–low estimate follows from a directional interaction identity pairing high-frequency mass with low-frequency energy, together with an induction on frequency.

Given an undirected, unweighted graph $G = (V,E)$ with $n$ vertices and $m$ edges, the triangle counting problem seeks the number of three-cycles in it. Triangle and subgraph counting are classical problems in graph algorithms, central to applications such as community detection, computing the clustering coefficient, motif discovery in protein networks, and social network analysis. In many of these applications, the graph datasets are so voluminous that we model them as streams of updates to an underlying graph. There are a number of foundational results for streaming triangle counting, both theoretical and practical. There is, however, one major drawback to all previous sublinear-space algorithms: to achieve both a constant factor approximation and the sublinear space guarantees, one needs to know a priori a constant factor approximation of the triangle count $T$, an inherently circular requirement. We initiate the study of parameter-free streaming triangle counting, without any a priori knowledge of $T$ or any quantities depending on $T$, provided $m$, the length of the stream. We describe a family of $O(p)$ pass parameter-free triangle counting algorithms that guarantee a mixed multiplicative and additive approximation of $T$ and use $\widetilde{O}(\frac{m+T}{\sqrt{T}})$ expected space. Moreover, this family leads to an $O(\log\log(n))$ pass algorithm that gives a $(1+\eps)$ multiplicative approximation of $T$ with the same space complexity. These algorithms rely on the notion of a verified parametrized algorithm: an algorithm parametrized by $\tau$ that either provides an approximation of $T$ when $\tau \le T$, or declares that $T < \tau$. Furthermore, we prove a lower bound: any parameter-free algorithm that provides a multiplicative approximation for all values of $T$ must use $\Theta(m)$ space, even on streams where the triangle count is moderately large.

We consider balanced doubly intermittent full-branch interval maps with two neutral fixed points. Although the ordinary empirical measures fail to converge for Lebesgue-almost every initial point, we show that this non-statistical behavior persists under a broad class of deterministic observation schemes: if the sampling sequence is block-dominating, then both endpoint Dirac masses are accumulation points of the sparse empirical measures for Lebesgue-almost every orbit. This class includes sampling sequences of positive lower density, arithmetic and polynomial sequences, and finite-index regularly varying sequences. We also prove that every map in the balanced class has the strong natural-measure property, including at the boundary exponent $\beta=1$: the pushforwards of every absolutely continuous initial probability converge to a distinguished endpoint mixture $\nu_*$. Abstract consequences of this one-time limit include annealed convergence along every deterministic sampling sequence, the existence of arbitrarily sparse sequences with almost-sure convergence, and an almost-sure convergence criterion based on a power-saving variance bound. For the symmetric quadratic map $g_1\in\mathfrak F_*$, we verify the dyadic covariance condition on a countable uniformly dense endpoint-flat family: the double covariance sum is $O_\phi(n\log(n+2))$. Consequently its dyadic empirical measures converge for Lebesgue-almost every initial point to $\tfrac12(\delta_{-1}+\delta_1)$.

Odd-frequency superconducting pairing has been predicted to arise in many systems and is known to lead to phenomena such as paramagnetic Meissner response and long-range superconducting proximity effect. Here, we provide generalized necessary and sufficient conditions for odd-frequency pairing appearing in any superconducting system, from bulk superconductors to superconducting hybrid structures. This generalizes an earlier first-order expression in multiband bulk superconductors to all superconducting systems and to all orders, in both order parameter and frequency. We then apply the derived conditions to several systems, including superconducting-ferromagnet and superconducting Josephson junctions, as well as a transition metal dichalcogenide monolayer proximitized by a conventional superconductor, where the generalized conditions are used to understand the properties of the superconducting state.

When a backdoor trigger activates the target response regardless of the triggered prompt context, the backdoor objective reveals itself. Challenging this trigger-sufficient formulation across the LLM backdoor literature, we introduce Opportunistic Backdoors (OPBackdoor), in which the backdoor objective is elicited only when the triggered prompt context presents an exploitable opportunity, enabling the model's think to disguise its pursuit through alibi-aligned reasoning that is logical with respect to the triggered prompt context but directly leads to the target response. Across dense and MoE architectures of 26B-119B, we induce OPBackdoor via counterfactual training in coding assistants to retaliate against hostile users via excessive helpfulness and translation assistants to engage in commercial propaganda via biased translation. Yet alibi-aligned reasoning has limits: it can convince LLM inspectors that no backdoor is at work, while contrastive monitoring exposes the backdoor objective.

LiDAR point clouds acquired in underground environments exhibit severe geometric incompleteness due to occlusions and limited sensor viewpoints, making reliable point cloud completion challenging without large supervised datasets. We propose ZVeC, a zero-shot, instance-driven framework that reformulates scene-level completion as compositional object-level reconstruction. By decomposing a scene into semantic object instances, ZVeC reduces reconstruction ambiguity in cluttered environments while eliminating the need for scenario-specific training. Each segmented vehicle is completed independently using a depth- and 3D Gaussian-conditioned diffusion model that exploits generalized geometric priors before the reconstructed instances are recomposed into the original scene. To evaluate our approach, we construct a real-world dense LiDAR benchmark of underground parking environments. Experimental results demonstrate consistent improvements over representative scene-level baselines in both quantitative metrics and visual quality. The completed point cloud differs substantially from the measured input (average KL divergence ~ 2.1), yet reducing the input to only 1% of the original LiDAR measurements changes the completed reconstruction only marginally (KL divergence < 0.50). This demonstrates that ZVeC produces geometrically consistent completions even under extreme input sparsity.

Quasinormal modes of black holes provide a central avenue for confronting general relativity with gravitational-wave observations from the ringdown stage of compact binary coalescence. However, inferring black-hole properties or the background spacetime requires theory-dependent input. In this work, we connect a calibrated eikonal method, which relates quasinormal mode shifts to local metric properties near the light ring, with the underlying rotating black hole metric. Using Bayesian inference, we compare a metric-specific model based on the global Konoplya-Rezzolla-Zhidenko parametrization with a metric-agnostic approach based on local values and radial derivatives of the metric functions at the light ring. For each model, we perform both one-parameter and simultaneous multi-parameter analyses and quantify how their priors map onto the same local quantities. We find that uniform priors on the metric-specific coefficients can induce strongly nonuniform and skewed priors on these local metric quantities. For an injection compatible with general relativity, both models remain consistent with Kerr and yield similar posteriors for the orbital frequency and Lyapunov exponent despite their different induced priors. For injections representing deviations from general relativity, one-parameter analyses can fail to recover the injected local deviations and, in the metric-specific case, can bias the global metric reconstruction. The multi-parameter analyses of both models yield consistent constraints, suggesting sufficient robustness. However, they introduce substantial degeneracies and differences in marginalized parameters, demonstrating practical limitations when considering more flexible models. Finally, we apply the framework to approximate posterior information from the ringdown analysis of GW250114 and find that both models are consistent with the Kerr metric in general relativity.

We introduce Graph Neural Automata Clustering (G-NAC), an unsupervised clustering method in which observations interact as cells on a fixed neighborhood graph. A shared recurrent graph-neural cellular rule evolves latent domain states through local interactions, which are converted into a rank-based spectral affinity for partitioning. Across 73 clustering tasks from 57 benchmark datasets, G-NAC achieved a mean adjusted Rand index (ARI) of 0.7951, comparable to Genie at 0.7941 and higher than the other evaluated baselines. Empirical training time and GPU memory scaled approximately linearly from 5,000 to 100,000 nodes. Learned transition rules also transferred from smaller source graphs to independent 100,000-node samples generated under matched conditions. These results demonstrate a recurrent graph-clustering formulation while identifying dependencies on graph quality, readout design, and source-target similarity.

A domain derivative for time-dependent electromagnetic scattering from perfect conductors is established. By proceeding through the Laplace domain, frequency-dependent bounds on solutions to Maxwell's equations are derived. These bounds are used both for establishing time regularity properties of the domain derivative and for proving convergence of the proposed Runge–Kutta convolution quadrature semi-discretization in time. A full convergence analysis is also carried out for pointwise evaluations of the domain derivative, when this time discretization is combined with a Galerkin method in space. Numerical experiments validate the convergence orders in time and space. Eventually, the domain derivative is applied in an iterative shape reconstruction algorithm, in which the electric near field is measured at some receiver positions away from the perfect conductor. Numerical examples show the feasibility of this algorithm and in particular highlight its robustness, when additional noise is applied to the data.

Linear probing and activation steering use linear directions to predict and control behavior-level concepts such as correctness, safety, and social bias in question answering. We call these behavior-level concept directions. Drawing on the Linear Representation Hypothesis (LRH) and circuit studies, these directions are often interpreted as concept representations. Yet controlled evidence for linear concept structure and local mechanisms does not establish that these directions represent the intended behavioral concepts, leaving probing and steering without a unified theoretical account. We propose the Answer-Basin Representation Hypothesis (ABRH): the model's own answer measure organizes the linear structure of these directions. For each question, all continuations yielding the same answer form an answer basin, whose mass is their total probability; these masses define the model's answer distribution. ABRH posits that its concentration before generation and the relative mass of each answer after generation are represented along linear directions shared across questions. Experiments span four Qwen2.5 and Gemma-3 models on correctness, social bias, and safety tasks. Decoupling concept labels from basin mass shows that probing and steering exhibit concept-consistent effects when labels align with mass orderings, weaken as mass gaps shrink, and reverse under conflict. Probes trained solely on mass orderings among wrong answers still select correct answers; a fixed steering vector can instead favor wrong answers when labels conflict with mass orderings. These results support an answer-measure account of behavior-level concept directions and of when probing and steering succeed, fail, or reverse.

We prove that any two rank-one systems are sub-$L^1$ orbit equivalent, fully clarifying quantitative orbit equivalence for a generic class of transformations. As a corollary, we establish unconditional optimality of Belinskaya's theorem.

Mesopic and low-light display transforms require, as their driving signal, a per-pixel scotopic-to-photopic luminance ratio (S/P); the exact spectral S/P is unavailable for ordinary RGB content, so a low-cost closed form that estimates S/P from a linear-RGB triplet is used in its place. Such closed forms exist but have been characterized only on narrowband / LED sources, i.e. spectrally sparse spectra, where a relative error of ~41% has been reported for a three-channel projection. Display content, however, is natural and broadband. We ask whether the same closed form is adequate there, using per-pixel spectral S/P from hyperspectral imagery as ground truth. On a daylight radiance time-series, a six-scalar closed form (three photopic and three scotopic channel weights) reproduces spectral S/P with a median error of ~0.07 that is time-invariant once the RGB input is chromatically adapted to D65; evaluated in un-adapted sRGB the error instead carries a color-temperature tilt across illuminants (~0.19), so adaptation is the enabling step for this use case. The result generalizes to an independent fifty-scene set (pooled median 0.024; 45/50 scenes within a pre-registered 0.10 band), with the few exceedances concentrated in saturated, spectrally-peaky surfaces that approach the narrowband regime (floral close-ups in this set). The scotopic weight vector is shown to be primary-model dependent, but the value used here is corroborated by a primary-free XYZ projection, and the median error stays within the band across all principled coefficient choices. We do not claim observer-validated appearance fidelity or adequacy on narrowband sources; both are out of scope. Both outcomes follow from the same three-channel projection: it is overwhelmed by spectrally sparse inputs and adequate on spectrally smooth ones.

Automated Speaking Assessment of non-native speech must effectively evaluate prosody, including speech rhythm, to align with human perception. However, commonly employed rhythm metrics rely on segmental duration, requiring an additional alignment step, which is error-prone in non-native speech containing disfluencies and mispronunciations. We propose an acoustics-based assessment approach that employs a convolutional neural network to extract rhythm features directly from the speech amplitude envelope, motivated by evidence linking low-frequency modulations to rhythm perception. The proposed models are trained on a proficiency score regression task using the speechocean762 dataset and compared against duration-based models. Our results show that a model using the amplitude envelope's first derivative achieves the highest correlation with human-assigned scores on the Fluency and Prosody dimensions, producing significantly lower errors than one using segment durations among less fluent speakers. The findings support acoustic envelope features as robust, alignment-free alternatives for L2 rhythm assessment. Code is released publicly.

The effective-field-theory interpretation of generalized uncertainty principle (GUP) deformations does not by itself establish whether their tree-level operator content is closed under renormalization. We investigate this question in massless GUP-deformed quantum electrodynamics at one loop and to first order in the deformation parameter. We compute the ultraviolet divergences of the relevant Green functions and determine the counterterm structure. Our central result is that, after eliminating redundant operators, renormalization requires only one independent physical counterterm beyond those associated with the tree-level theory: an axial-axial four-fermion interaction. The original deformation therefore admits a minimal radiative completion at this order. We derive the leading logarithmic running of the induced Wilson coefficient and discuss its operator-level correspondence with the contact interaction arising in Einstein-Cartan gravity.

A well-known phenomenon in statistical analyses of populations of phylogenetic trees in the Billera-Holmes-Vogtmann space is that the topology of the Fréchet mean tree can contain multifurcations (i.e., internal nodes with more than two children), which raises the practical question of whether this reflects a population-level branching structure (hard polytomy) or merely sampling variability in the data (soft polytomy). This is an instance of the more general phenomenon of "stickiness" in non-Euclidean statistics, whereby the sample Fréchet mean in certain non-positively curved stratified spaces becomes permanently trapped in a lower-dimensional stratum. In this work, we identify a particular multidimensional random walk embedded within the Fréchet mean process, and we show that the time at which stickiness occurs is determined by the largest last-passage time above zero of the coordinates of this random walk. Using this representation, we develop a fully nonparametric procedure for estimating the probability that trifurcations in a sample Fréchet mean tree will bifurcate at some future time if more observations are collected. Lastly, we apply our methodology to a problem in phylogenetics where we consider whether an observed trifurcation in the species tree of primates, glires, and tree shrews is genuinely trifurcated at the population level.

Scalable simulation is essential for robot data generation, policy training, evaluation, and safe iteration, yet real-world interaction is costly and conventional simulators require labor-intensive construction. We present Uranus, a data-driven robot simulator built around a joint-trajectory-conditioned autoregressive diffusion model. Uranus offers three key capabilities: (1) streaming, open-ended rollout, which receives future joint-position trajectories online and autoregressively generates one latent frame per step, corresponding to four RGB frames, without a fixed horizon; (2) low-latency generation, achieving 24 FPS after inference optimization; and (3) scalable, extensible robot control, providing a unified interface for synchronized multi-view generation across diverse robot embodiments and camera configurations. We conduct comprehensive quantitative and qualitative evaluations on both in-distribution and out-of-distribution data, providing an objective assessment of Uranus and clearly identifying its current limitations. We release the code and model weights to empower the community with practical tools and insights.

Advances in processing power, camera technologies, and mobile image analysis have made smartphones and other mobile devices, such as laptops, increasingly suitable for medical diagnosis and healthcare applications. Researchers have developed low-cost solutions for the early detection and monitoring of various health conditions, including eye and ENT diseases, malnutrition, heart rate variability, skin and oral conditions, and injuries, using images captured by non-medical devices such as smartphones and webcams. This survey examines existing research on mobile image-based medical diagnosis, with an emphasis on its potential to enable low-cost and accessible healthcare. We comparatively analyze state-of-the-art solutions across different healthcare application categories, examining their advantages and limitations. Based on this analysis, we identify desirable characteristics of mobile image-based diagnostic tools and highlight areas where existing approaches have made progress as well as areas requiring further research. We also discuss application-specific and common challenges and outline directions for future research. Overall, this study provides a comprehensive overview of mobile image-based healthcare solutions and their potential to support low-cost disease diagnosis and monitoring, particularly for underserved populations in remote and resource-constrained settings.