Papers

The recent report of xenon recoiling with about 250 keV energy at the LUX-ZEPLIN (LZ) dark matter direct detection experiment was interpreted by the collaboration as either a momentum-dependent elastic scatter or an inelastic scatter. In this study, we take elastic operators listed by LZ that fit the datum to better than 3$\sigma$, and perform a log-likelihood analysis using the reported significances to reverse-engineer their best-fit Wilson coefficients. We then estimate for each operator the rate of dark matter capturing in the Sun, and assuming self-annihilations of the captured population to various Standard Model final states studied by IceCube, we place limits on them from measurements at IceCube and Super-Kamiokande of the fluxes of solar-direction and atmospheric neutrinos. We show that large ranges of dark matter masses around the weak scale that explain the LZ event are ruled out in this scenario, with the strongest limits placed by direct annihilations to neutrino-antineutrino final states.

While object detection has advanced through improved architectures and open-vocabulary models, we provide strong evidence that benchmark quality is limited by annotation incompleteness. Across four widely used datasets (COCO, Pascal VOC, Cityscapes, KITTI), re-annotation reveals substantial increases in annotated objects (e.g., up to +60% on KITTI and +40% on COCO), driven primarily by previously unlabeled small, occluded, or densely packed instances. While some differences arise from dataset-specific annotation conventions, we consistently find that missing annotations are the main source of label errors across all datasets. To achieve high data quality, we introduce a scalable annotation pipeline that emphasizes high recall and captures ambiguity through soft labels aggregated from at least 11 annotators per object. The resulting annotations improve coverage and align well with human calibration. We show that benchmark performance is highly sensitive to annotation quality, although model rankings remain largely stable. We introduce two large-scale benchmarks: (i) an uncertainty-aware object detection benchmark, and (ii) a label error detection benchmark grounded in real label errors. We show that current detectors are strongly depended on annotation quality and are misaligned with human perception. Current label error detection methods, which have been shown to perform well on synthetic noise, struggle to achieve high recall and precision on real label errors. Our results highlight the need for future object detection benchmarks to move beyond deterministic annotations toward high-recall, uncertainty-aware evaluation that maximizes valid instances and better reflects real-world ambiguity.

This paper presents a novel framework integrating Bayesian physiological modeling with optimal control strategies to achieve uncertainty-aware, personalized hemodynamic regulation during fluid resuscitation. An uncertainty-aware variational autoencoder state-space model (UVAE-SSM) was first developed to capture the dynamical relationship between mean arterial pressure (MAP) and fluid infusion using limited data, while explicitly modeling aleatoric uncertainty (i.e., randomness in the measurements, such as sensor noise). Then, a Bayesian nonlinear state-space model (BNSSM) was developed by utilizing Bayesian neural networks (BNNs) to capture epistemic uncertainty arising from physiological and patient-specific variability, enabling the creation of a virtual patient generator (VPG). Building on this uncertainty-aware modeling framework, a stochastic radial basis function model predictive control (sRBF-MPC) algorithm was designed to track the MAP target while satisfying physiological constraints. Finally, an online fine-tuning algorithm was developed to adapt the nominal UVAE-SSM using streaming VPG data, enabling progressive personalization during closed-loop therapy. Simulation results across unseen animal subjects and an independent human clinical dataset demonstrated the strong predictive accuracy and cross-population generalizability of the UVAE-SSM and BNSSM models. Closed-loop evaluations confirmed that the proposed sRBF-MPC framework achieved stable MAP regulation while providing better risk-aware control compared to quadratic MPC (Q-MPC) and stochastic quadratic MPC (sQ-MPC). Overall, the proposed framework accounts for inter- and intra-patient variability through online model adaptation, offering a promising step toward uncertainty-aware, personalized hemodynamic modeling and control in critical care.

The energy dissipation law and the maximum bound principle (MBP) are two important physical features of the well-known Allen–Cahn equation. In this paper, we develop and analyze novel second-order linear numerical schemes for a class of Allen–Cahn type gradient flows. Our scheme is based on the generalized scalar auxiliary variable (GSAV) approach and a novel second-order exponential time-differencing Runge–Kutta (ETDRK2) method. The resulting formulation overcomes a longstanding difficulty in combining these two techniques while retaining both the MBP and energy stability. We prove that the proposed scheme unconditionally preserves both the MBP and energy stability. In addition, rigorous error analysis is carried out for the proposed scheme, establishing second-order accuracy in both time and space without imposing any coupling condition between the time step $\tau$ and the spatial mesh size $h$. We also present some numerical experiments to demonstrate the efficiency of the proposed scheme and its preservation of the theoretical properties.

Prussian blue (PB) and its analogues (PBAs) are interesting materials for electrochemical applications due to their tunable redox chemistry and open framework structure. In this study, hexacyanoferrates (HCF) containing iron (FeHCF), cobalt (CoHCF) and nickel (NiHCF) were synthesized via potentiostatic electrodeposition. Cyclic voltammetry revealed distinct redox behaviors. Morphological characterization (SEM, EDX) demonstrated uniform, pyramidal film growth for FeHCF and CoHCF. Otherwise, NiHCF presented a cracked film with cubic clusters on top due to residual stress. Despite this, homogeneous element distribution was found for all samples. Structural characterization (TEM and XRD) confirmed a cubic lattice crystal structure for all films, with systematic lattice contraction from Fe to Co to Ni due to decreasing atomic radius. Raman and XPS data revealed a shift toward Fe2+ dominant oxidation states and modifications in CN bonding with the influence of K+ and water occupancy in the PBAs framework. These findings illustrate how metal substitution and deposition parameters can tune the structural and electrochemical properties of PBA films, presenting a strategic route to design tailored electrodes.

The performance improvement of high-power flat BLDC motors has accelerated the development of dynamic robots. However, many commercially available servo motors assume operating voltages of 48 V or lower, which limits the maximum rotational speed. Dynamic robots require rapid energy generation, so this voltage constraint restricts motion performance. Therefore, driving motors beyond the rated voltage is desirable to increase the instantaneous maximum speed. On the other hand, semiconductor devices used in motor drivers have a trade-off between voltage rating and current capacity. Conventional drivers using Si MOSFETs have difficulty achieving both high-voltage and high-current operation. Although GaN FETs are promising, compact drivers that can be mounted on the rear side of flat BLDC motors remain limited. In this study, a motor driver for high-power flat BLDC motors using GaN FETs is developed. The effect of delay compensation in the high-speed region beyond the rated operating range is also investigated. In the experiments, under 96 V operation, a continuous current of 30 A was achieved with a heat sink attached. A peak current of 80 A and a maximum electrical frequency of 3110 Hz were confirmed. A high-speed load lifting experiment driven by a LiPo battery 24S (100 V) was also conducted, demonstrating applicability to dynamic robot operation.

Vision-Language-Action (VLA) models have become a prominent paradigm for mapping multimodal inputs, including semantic instructions, visual observations of the scene, and proprioceptive observations, to robot actions. Most state-of-the-art models predict actions in the end-effector pose space as sequences of action chunks. Training and evaluating these models requires large-scale collections of real-world demonstrations, pairing robot actions with the corresponding visual and proprioceptive observations. Collecting such data on real hardware typically relies on human teleoperation, making the process costly, time-consuming, and difficult to scale. We present an open-source sim-to-real experimental protocol that addresses this bottleneck: expert trajectories generated in simulation are replayed open-loop on a real Franka FR3 setup, where the corresponding real visual and proprioceptive observations are recorded and converted into a format compatible with VLA training. The same deployment stack is then reused, in closed-loop, to evaluate a trained policy on that setup, so that data collection and evaluation share an identical hardware configuration. Because each real recording is paired with the simulated trajectory that produced it, the protocol also yields a direct measurement of the sim-to-real gap. We release the collected datasets on Hugging Face together with the pipeline source code https://gitlab.isir.upmc.fr/kappel/sim2real_public_chunk_control.

In many delivery settings, a receiver may already hold a lower-quality or lower-resolution representation of an image, obtained through an independent transmission. Conventional codecs encode a subsequently requested higher-quality representation without exploiting this incidental side information, whereas conditional codecs generally assume a prescribed source of side information that is always available. We instead consider an opportunistic setting in which side information may or may not be present. We introduce a single entropy model that conditions on a previously decoded latent when available and falls back to a standard hyperprior otherwise. The proposed adapter maps the side-information latent to the prior signal required by the entropy model, allowing the same model to support multiple target and side-information quality combinations. The analysis and synthesis transforms remain frozen, enabling retrofitting of an existing learned codec while preserving its latent representation and reconstruction path. When the receiver holds the quality immediately below the target, the proposed method reduces the rate of the subsequent transmission by up to 46%, or by 52% when an additional hyper-latent is transmitted. In the absence of side information, the rate penalty remains below 4%, and the reconstructions are bit-identical across the conditional and fallback modes.

Adaptive optimization methods such as AdaGrad and Adam are widely used in modern deep neural network training, but their adaptive scaling is primarily designed for vector-valued parameters and does not explicitly exploit matrix structure. Recent matrix-aware optimizers demonstrate the benefits of structured optimization, yet a general theoretical framework for deriving matrix-aware adaptivity comparable to that of AdaGrad remains lacking. In this work, we develop an Online Mirror Descent framework with adaptive proximal functions for matrix-valued parameters, providing a principled methodology for deriving matrix-aware adaptive optimization through online regret minimization. By introducing row-wise and column-wise matrix proximal functions, our framework explicitly reveals the trade-off governing adaptive scaling: increasing the scaling factors reduces the gradient-dependent dual norm term while increasing the cost of evolving the proximal geometry. In the row-wise setting, this trade-off becomes separable under diagonal parameterization, allowing the adaptive scaling for each row to be derived independently by minimizing its corresponding row-wise regret bound. The column-wise counterpart follows directly by applying the row-wise construction to the transposed matrix. This framework yields Row-wise Matrix AdaGrad and Column-wise Matrix AdaGrad as concrete instantiations, with regret guarantees that are strictly tighter than those of entry-wise AdaGrad under row-sparse or column-sparse gradient structures. Experiments on matrix factorization and stacked deep MLP training further demonstrate the benefits of matrix-aware adaptive scaling, yielding improved optimization performance in both settings and enhanced optimization stability and trainability at larger learning rates and greater network depths in the latter.

We investigate a quantum cosmological model within the framework of Einstein-Æther gravity. The model consists of a positively curved FLRW Universe whose matter content is described by pressureless dust, in the presence of a positive cosmological constant $\Lambda$. We first perform a classical Hamiltonian analysis utilizing Schutz's variational formalism, constructing the phase space and deriving the dynamics of the scale factor. Notably, we identify specific initial conditions that lead to an inflationary expansion, which are extracted directly from the underlying quantum dynamics. Subsequently, we quantize the model following Dirac's formalism, obtaining the Wheeler-DeWitt equation for the wave function of the Universe. We solve this equation numerically and investigate our results using the WKB approximation to compute quantum tunneling probabilities $TP_{WKB}$ alongside an integrated tunneling probability $TP_{int}$, the expectation value of the scale factor coming from the many-worlds interpretation of quantum mechanics, and the corresponding Bohmian trajectories. Finally, we analyze how these tunneling probabilities are influenced by variations in the fundamental parameters of the theory ($\Lambda, \sigma, \beta$) and the energy of the dust fluid $E$. Our results indicate a higher probability for the quantum birth of the Universe in a parameter regime characterized by larger values of $\Lambda, \sigma$, and fluid energy $E$, together with a smaller value of $\beta$.

In classification-oriented adaptive sensing, posterior samples characterize uncertainty at the current measurement state and can serve two roles: they may guide the next sensing direction, while their class labels provide votes for the candidate classes and determine whether sensing should continue. We focus on the stopping layer that turns these votes into a declaration, without modifying the posterior sampler or sensing directions. A natural plug-in rule declares when the observed vote share exceeds a threshold. We show that this threshold is not itself a confidence guarantee: when the underlying vote mass equals the threshold, the plug-in rule declares about half the time. As alternatives, we calibrate a fixed-sample rule and a finite-horizon sequential rule to a prescribed false-declaration probability, and study exact curtailment, which stops a fixed-pool rule once its final verdict is forced. We then derive how one-round declaration probabilities determine posterior-sample cost and classification accuracy along a sensing path. On MNIST with DDRM and a fixed PCA-guided probe sequence, curtailment saves up to 62% of posterior samples. Among the evaluated rules at matched operating points, sequential stopping reduces the cost the most. At a high accuracy, that same sequential rule can trade more posterior samples for fewer measurements.

Recent advances in diffusion models have enabled high-performance, instance-adaptive compressed sensing through posterior sampling, without task-specific policy training. Existing methods select sensing probes by maximizing total posterior signal variance and are therefore primarily reconstruction-driven. We introduce a classification-driven extension motivated by the closed-form posterior covariance of a class-conditional Gaussian mixture model, which decomposes into within-class and between-class uncertainty. Using calibrated soft classifier outputs, we estimate these uncertainty terms from diffusion posterior samples and propose a classification-oriented criterion for selecting the dominant sensing direction in the unmeasured subspace. Experiments on MNIST and CIFAR-10 compare the resulting classification accuracy, measurement cost, and reconstruction quality with those of reconstruction-oriented counterparts. The results identify regimes in which semantic posterior uncertainty yields a more favorable classification–measurement trade-off and quantify the associated reconstruction cost.

Multimodal survival models can combine complementary prognostic information from whole-slide images and genomic profiles, but effective fusion remains challenging amid external cohort shift and computational complexity. To address these challenges, we propose MIST, multimodal survival prediction with genomic-guided histology attention. MIST represents genomic features as tokens and allows them to query compact foundation-model-derived histology context tokens before survival prediction. This design enriches molecular information with histology context rather than merging separately encoded modalities only at the final stage. Training combines discrete-time survival prediction with genomic feature masking, WSI dropout, and paired WSI-genomics contrastive alignment. Across four external evaluations in colon, renal, lung, and glioblastoma cohorts, MIST improves external C-index over standard fusion baselines in the primary comparisons. These results support genomic-guided histology attention as a compact and effective strategy for multimodal oncology outcome prediction. Our code is available at https://github.com/samiyavuuz/MIST .

Exoplanets orbiting active stars offer distinct challenges for atmospheric characterization with the JWST. Among such worlds, the hot Saturn WASP-52b (Teq $\sim$ 1300 K) orbits an active K-dwarf that complicates transmission spectroscopy via stellar contamination. Here, we present the first JWST NIRSpec G395H (2.8-5.2 $\mu$m) limb-averaged transmission spectrum of WASP-52b obtained through the JWST Morning/Evening Program (GO-3969), which aims to measure limb asymmetries across a sample of hot giant exoplanets. Our spectrum reveals the first detection of CO2 in WASP-52b's atmosphere (ln B = 54 / >10$\sigma$), with a retrieved abundance of log CO2 = -5.09 (+1.09/-1.14), and confirmation of H2O (ln B = 5.9 / $\sim$3-4$\sigma$), with an abundance of log H2O = -3.41 (+1.04/-1.04). We find a steep near-infrared slope that cannot be explained solely by H2O absorption, necessitating unocculted starspots (ln B = 5.7 / $\sim$3-4$\sigma$), though with properties that vary across our data reductions. The shape and amplitude of the molecular bands additionally suggest high-altitude (<10 mbar) inhomogeneous clouds covering $\approx$40 $\pm$ 20% of the terminator (ln B = 1.9 / $\sim$2.5$\sigma$), which provides evidence for the morning-evening terminator differences that are consensus predictions of general circulation models. WASP-52b's atmospheric metallicity can be sub-solar, solar, or super-solar, due to order-of-magnitude uncertainties in the molecular abundances caused by degeneracies between compositions, clouds, and starspots. However, chemical equilibrium retrievals provide a similar statistical fit while favoring a tighter constraint with a super-solar metallicity (M/H = 14.8 (+12.1/-5.9) $\times$ solar). These results highlight that chemical detections and aerosol properties may still be recovered for planets orbiting active stars via transmission spectroscopy.

Learning is a social act and requires communication between interlocutors. Learning also is a process that takes place in a socially constructed neuro-normative system. These neuro-normative systems can marginalize neurodivergent (i.e., autistic, dyslexic, epileptic) students, who oftentimes have non-normative socio-cognitive differences. In this paper, we investigate 3 neurodivergent physics students' experiences solving physics problems and in navigating physics culture. We use a Double-Empathy Problem lens to uncover how communication, particularly communication breakdowns, can manifest for neurodivergent students in physics. We find that the 3 neurodivergent students, experience communication breakdowns in their interactions with peers and instructors, and in interpreting problem statements. Furthermore, we find that participants experience fear of being misinterpreted or feeling as though they are viewed as stupid on account of communication breakdowns. We advocate for the need for a more neuro-inclusive physics culture and suggest actions that can be done by instructors to create more inclusive spaces in physics.

In a series of three papers, we propose a distributed intraday scenario planner for energy management in 16.7-Hz single-phase railway power systems, which is realized and tested as open-source Python package bahnstrom\_ems v0.9.1~\cite{noroozi2026bahnstromems}. The first part of our work develops the mathematical foundation of a two-layer energy management system for a 16.7-Hz single-phase railway power network, where the network is an interconnection of several control areas. Given a day-ahead plan, last measurement of stored energy in wayside batteries, and forecasts of the tractive, available regenerative and renewable energy, we provide a mathematical modeling of the 16.7-Hz single-phase railway power network in intraday time-scale. Then the intraday energy planning is formulated as a risk-neutral convex stochastic optimization problem. The overall intraday planning is shown to be a sparse convex quadratic program. Hence, this problem is solved recursively by adopting a distributed scenario model predictive control (MPC) setting. The corresponding area problems, coordination updates, convergence conditions, and closed-loop key-performance-index accumulation are derived. In practice, detailed train-motion and microscopic motoring and regenerative-power profiles are usually needed for accurate simulations and numerical validation purposes. Due to data privacy and security concerns, detailed train-motion and microscopic motoring and regenerative-power profiles in a real power network may not be publicly available. Therefore, we also propose a mathematically-solid approach to estimate such microscopic information by converting causally timetable and train-motion information into quarter-hour motoring and regenerative-power profiles while exactly preserving each hourly energy target.

We prove that a smooth projective surface over $\mathbb{C}$ admitting a full exceptional collection of line bundles is rational.

Weak gravitational lensing by intervening structure magnifies distant sources and induces scatter in their luminosity distances. We develop an improved stochastic model for the distribution of weak lensing magnifications, $\textrm{d} P/\textrm{d}\mu$, that resolves individual lenses down to $10^7\,M_\odot$ and, besides field halos and filaments, includes subhalos and large-scale clustering of the lens population. Both extensions broaden the distribution, and the resulting scatter in the luminosity distances agrees with the halo-model prediction from the nonlinear matter power spectrum and exceeds that obtained from $N$-body ray tracing, which we attribute to the finite mass resolution and box size of the simulations. Building on this model, we develop FLUMEN, a conditional normalizing-flow emulator that predicts $\textrm{d} P/\textrm{d}\mu$ as a function of the source redshift up to $z_s=12$ and six cosmological parameters $\{h,\Omega_M,\Omega_B,\sigma_8,n_s,z_{\rm eq}\}$. The emulator enforces the empty-beam cutoff and the fold-caustic power-law tail analytically, and reproduces the Monte Carlo distributions on held-out cosmologies with a median Kullback–Leibler divergence of $0.0048$ at negligible cost. Our results provide a fast and flexible framework for incorporating the full magnification distribution into cosmological analyses of standard sirens, supernovae, and other point sources.

Background: Just-in-time adaptive interventions (JITAIs) can use behavioral data to adapt support to changing contexts, but many rely on predefined rules and manual configuration. Objective: We developed a proof-of-concept sleep JITAI using an AI agent to review personal data, evaluate reminders, adapt interventions, and record decisions for human review. Methods: Running in Home Assistant on a configurable schedule, the agent follows a reusable skill file to review 30 days of sleep and behavioral data, including physical activity, smartphone use, and bedtime routines, to identify patterns and create or update automated reminders. Results: Initial runs demonstrated technical feasibility, successfully completing data review and intervention decisions while limiting reminders to three per day and saving decision records. Conclusions: Agentic AI may enable flexible, adaptive sleep JITAIs. The architecture supports future comparison with fixed or rulebased interventions, requires human oversight, and could extend to other health behaviors.

Given a compact semantic scene graph, long-term indoor video relocalization estimates a map-frame trajectory after lighting and furniture changes. Visual methods rely on appearance and become unreliable under these changes; localizing one frame at a time from object classes and geometry instead leaves sparse, ambiguous evidence. We introduce VideoReloc, whose adaptive clips use odometry to gather spatial evidence until object and motion criteria are met, adapting query length to the observed scene. Its run-level decision rechecks conflicting placements using evidence accumulated across connected clips, stabilizing the trajectory beyond adjacent-clip tracking. Hypothesis-first registration proposes poses from object triplets and verifies each using clip-wide object centers and box surfaces. Orientation-aware refinement uses box faces, gravity and wall directions to resolve ambiguity in camera orientation and refine the full pose. This reframes sparse-map relocalization as verification of spatially extended video queries, moving discriminative support from stored appearance to temporal context and permitting a 100 kB map of class-labelled boxes. On RIO10 and ReplicaCAD, the all-frame localization success rate at 1 m/10$^\circ$ is 73.5% and 61.1% under causal evaluation, rising to 90.6% and 74.8% with clip closure. The evaluated per-frame scene coordinate regressors reach up to 47.6% and 49.8%, respectively, with maps of 12.6-42 MB. Project page: https://videoreloc.github.io

Contact-rich motion planning (CRMP) is essential for robotic manipulation and locomotion, yet remains computationally challenging due to combinatorial contact decisions. Existing methods typically avoid broad evaluation of contact-mode sequences through search heuristics or optimization reformulations. We revisit broad evaluation in light of modern GPU hardware and introduce Contact-Mode Expansion with parallel Trajectory optimization (CoMET), which combines GPU-parallel trajectory evaluation with greedy contact-mode expansion. On planar pushing benchmarks, CoMET is competitive with optimization-based, sampling, and tree-search baselines in solution quality and planning time, matching the full-enumeration reference on nearly all instances with fewer evaluations and shorter planning times. Ablations suggest that much of the performance gain comes from the high-throughput trajectory evaluator. In bimanual nonprehensile manipulation, GPU-friendly local mode expansion achieves higher planning success than the tested adaptive tree search as the mode space grows. These results demonstrate that broad explicit mode evaluation provides a simple yet effective alternative for CRMP.

This paper presents the executable Python implementation~\cite{noroozi2026bahnstromems} of the two-layer energy-management system developed mathematically in Part~I~\cite{norooziP1I}. If needed, the software first converts hourly railway operation data into a 15-minute timetable and calculates the amount of gross motoring and available regenerative powers in a 15-minute resolution using the timetable handling layer. Given the energy prices are available either cached or by sending request to ENTSO-E Transparency Platform through the price handling layer, the software constructs a deterministic hourly day-ahead plan with either in the so-called must-run converter commitment mode or in a sub-hourly feasibility certificate mode, where the latter demands more computational complexity. Given the inputs from the timetable, day-ahead planning layers and initial measurements of the intraday-resolutions network's physical quantities, the intraday handling layer executes a risk-neutral intraday scenario model predictive controller by area-wise quadratic programming and consensus ADMM. The retained S1 forecast layer combines a one-week seasonal-naive point forecast with causal residual-path resampling, while the current measured stage is identical in all scenarios. We specify the day-ahead and intraday data contracts, absolute-UTC coupling, local-QP assembly, warm starts, residual tests, solver-status interpretation, physically validated recovery paths, state and key-performance-index updates, and the railway-owned generation and pumping increment. The Python package version, dependency lock, frozen-plan manifests, deterministic fixtures, run logs, and hash manifests make the software configuration executable and auditable. The paper describes software behavior and numerical contracts; comparative performance and stress-test claims are reserved for Part~III~\cite{norooziP1III}.

We study how far a simple statistical pipeline can go on univariate time series anomaly detection under a strict selection protocol. The method extracts a small pool of statistics over sliding windows, scores each window with a transductive robust (MAD) model, and selects a feature subset per domain on a held-out tuning split. On TSB-AD-U it reaches $0.529$ per-series VUS-PR, above the best neural ($0.45$) and statistical ($0.44$) entries on the public leaderboard and within $0.06$ of the strongest pretrained foundation model, several of which use more supervision than ours. Ablations locate the cause: across three selection strategies and a hindsight oracle the score moves by $0.031$, and across the aggregation grid by $0.096$, while changing the candidate pool moves it by $0.226$. The candidate pool sets the ceiling; the search over it is second-order. We therefore generate a pool per domain by prompting a multimodal LLM with in-context example windows from that domain. The generated pools match the hand-crafted one under matched selection, and the two cover different domains: selecting over their union improves on the generated pool in all twelve generator-seed pairs and lifts the pipeline to $0.588$, matching the performance of the best entry on the leaderboard.

Traditional unsupervised anomaly detection (UAD) methods are designed to flag or localise deviations from a normative distribution, ignoring the underlying generative mechanisms of the anomalies. Yet the nature of an anomaly is often as important as its presence. We reformulate UAD as a Bayesian inverse problem, in which the objective is to infer the most probable corruption responsible for each observation. Our framework yields a probabilistic anomaly score as the energy of the inferred corruption parameters, and serves as a principled recipe for developing new UAD algorithms. We derive several existing methods as instances of the general framework, each corresponding to the same energy score under different modelling choices. Experimentally, we study the framework's components in a controlled setting, and improve object-class AUROC on the MVTec AD dataset by 2.3% by adapting the underlying corruption model. Finally, we validate the framework on a brain MRI benchmark, achieving strong detection performance while producing estimates of pathology intensity, bias, and geometry. Code is available at https://github.com/jgmyles/inverse-uad.

Previous research suggests that the congruency between common VR input devices - such as controllers or hand tracking - and their visual representations (e.g., hand or controller avatars) influences user experience and performance. However, the specific effects of input-avatar combinations remain underexplored. We study the effects of common input devices (hand tracking and controllers) and visual representations (hand and controller avatars) on performance and perceived success in target acquisition tasks. We included both grasping and pinching gestures across 16 combinations of input, avatar, and target size. Results indicate that hand tracking benefits from any form of visual representation - even when mismatched - achieving up to 5.8% greater accuracy compared to having no avatar, likely due to its reliance on visual feedback in the absence of a physical prop. Controllers were generally preferred and offered faster task completion. However, mismatched avatars had a stronger negative effect with controllers, particularly when the virtual gesture did not align with the physical action, leading to a 5.6% drop in accuracy compared to the matched condition - suggesting that inaccurate feedback can be more disruptive than having no avatar feedback at all.

Architectures with periodic activation functions have already been shown to be beneficial in comparison to monotonic counterparts for a wide range of applications of physics-informed neural networks. Here, we investigate a network architecture which uses the complex exponential function, generating pairs of sine and cosine outputs as activation functions. Testing it against comparable, sine-activated multi-layer perceptrons for the task of temperature reconstruction from sparse velocity data for cubic Rayleigh-Bénard convection reveals significant improvements in the reconstruction quality without a substantial increase in computational cost per training step. Vice versa, the improved architecture enables reaching similar reconstruction qualities for a fraction of the expense. Analyzing the mathematical structure of these networks points to the improvements being rooted in the property of passing both a sine and cosine function forward. This way, the subsequent layer is able to adapt the phase of the provided latent periodic functions, and doing it individually for each of its neurons.

Static, asymptotically flat, non-Schwarzschild black holes in four-dimensional Einstein–Weyl gravity have previously been constructed by numerical shooting, while the local field equations admit horizon and asymptotic expansions to all orders. We give a computer assisted existence proof for a non-Schwarzschild solution whose dimensionless horizon radius is one in units of the massive spin-two scale, with parameters close to the previously reported numerical branch. The exterior is divided into a convergent horizon series domain, a compact core enclosed by a parameter dependent Taylor model with directed rounding, and an infinite asymptotic domain treated as a centered stable manifold fixed point problem. The resulting five-dimensional matching map between the core and tail is continuous on a rigorously specified box. After exact rational preconditioning, the signs on opposite faces satisfy the hypotheses of the Poincaré–Miranda theorem and therefore certify a matching zero. The solution has a regular nondegenerate horizon, no additional exterior horizon, vanishing Ricci scalar, and nonvanishing Ricci tensor. The strict bounds also imply existence at every sufficiently nearby horizon radius and a local continuous family at fixed coupling. The proof establishes existence, but not uniqueness of the matching zero.

Quantum superresolution promises to resolve closely spaced sources beyond the diffraction limit, but existing approaches generally rely on idealized source properties or require simultaneous estimation of multiple coupled parameters of realistic sources. Here we introduce a parameter-decoupled measurement that determines the separation of realistic passive sources without estimating their unknown brightness imbalance, mutual coherence, or relative phase. A complete four-parameter quantum Fisher information analysis identifies the separation information that remains accessible in the presence of these nuisance parameters. We then construct a directly measurable log-probability invariant from projection channels whose conditional statistics depend only on the separation. Broad classes of channel pairs satisfy the resulting decoupling condition. For a Gaussian point-spread function, the lowest-order implementation asymptotically attains the four-parameter quantum limit per incident signal in the sub-Rayleigh regime. This approach converts a realistic multiparameter imaging problem into an operationally single-parameter measurement, providing a practical route toward quantum-enhanced resolution of passive spatial, temporal, and spectral signals.

Modeling stiff string vibrations is challenging due to their dispersive and high-frequency characteristics. This study investigates the effectiveness of Physics-Informed Neural Networks (PINNs) in simulating the transverse vibration of a one-dimensional linear stiff string with sharp initial conditions induced by plucking. The governing Partial Differential Equation (PDE), along with the associated initial and boundary conditions, is incorporated directly into the loss function of the neural network. For the reference measurements, a wire-breaking experiment was performed to excite the string, and its vibration response was captured using a laser profiler. The comparison between experimental measurements, finite-difference time-domain (FDTD) simulations, and PINN-based simulations shows good overall agreement, highlighting the potential of PINNs for modeling stiff-string vibrations.

We study the minimisation of the first solenoidal Maxwell eigenvalue $\lambda_1^{\mathrm{Max}}(\Omega)$ in perfectly conducting cavities through the scale-invariant functional $J_{\mathrm{Per}}(\Omega):=\mathrm{Per}(\Omega)\lambda_1^{\mathrm{Max}}(\Omega)$ in $\mathbb R^3$. While the corresponding unconstrained problem is known to be ill-posed, we focus on bounded convex cavities and prove that $J_{\mathrm{Per}}$ has a positive infimum in this class. The proof relies on the study of the three main possible behaviours for sequences of convex domains with bounded perimeter: convergence to a bounded convex domain with non-empty interior, collapse to a planar domain or collapse to a one-dimensional domain. The case of a planar collapse is handled through projection arguments, yielding a lower bound, while that of a one-dimensional collapse is controlled by a different argument, showing that in that case $J_{\mathrm{Per}}$ must diverge to $+\infty$. Although the question of existence of an optimal shape (that is, ruling out planar collapse) remains open, we derive several negative results and obstructions to existence; typically, no minimiser with a $\mathscr C^{3,+}$ boundary exists. This relies on a fine analysis of first-order optimality conditions.