Fusion

arXiv:2610.21395v1 Announce Type: cross Abstract: Recent studies have reported promising results from quantum machine learning in drug discovery, healthcare, materials science and cybersecurity. Research includes comparisons with classical machine learning algorithms and the development of sequential hybrid pipelines. We propose that fusion can improve prediction when classical and quantum representations capture complementary predictive relationships. Our classical-quantum multi-view framework preserves the full classical input alongside simulated quantum features. Concatenation and attention combine the views; single-view comparisons, replacement and masking controls assess both views' contributions. Controlled angular-polynomial experiments distinguish targets adequately represented by a single view from those that depend on complementary angular and polynomial relationships. For these mixed targets, fusion improves prediction by combining the relevant feature sets, while matched

arXiv:2610.21990v1 Announce Type: new Abstract: Semantic segmentation is critical for the reliable navigation and scene understanding of autonomous systems, yet its performance degrades severely in low-light environments due to noise, artifacts, colour distortion, and the loss of structural details. To address this challenge, this work introduces VesperMUSE, a unified multi-modal framework designed to achieve robust image semantic segmentation across diverse illumination conditions. The architecture addresses the light deficiency problem on multiple levels: proposing a novel physics-informed synthetic low-light adaptation algorithm, approaching Low-Light Image Enhancement (LLIE) through a thermal-guided paradigm, and finally aiming to recover the intrinsic scene details for semantic segmentation through a multi-modal fusion approach. Following enhancement, the framework dynamically integrates the primary RGB features with auxiliary thermal representations and surface normal maps

arXiv:2610.21353v1 Announce Type: new Abstract: Accurate localization is critical for a wide range of applications, including navigation, augmented reality, safety management, and healthcare. However, achieving accurate positioning remains challenging, especially in GPS-degraded environments. Pedestrian Dead Reckoning (PDR), which estimates position based on inertial sensor data, offers a promising alternative but typically demands substantial effort in algorithm design and system integration. This complexity often leads to prolonged development timelines and elevated costs. To address these challenges, we present the PDR Sensor (PDRS), a plug-and-play, black-box PDR module optimized for seamless integration into wearable and IoT platforms. PDRS integrates a microprocessor and an inertial measurement unit (IMU) within a compact, low-power module. It executes real-time sensor fusion onboard, providing position updates via standard interfaces (I2C, SPI, UART). Experimental evaluations

arXiv:2610.21034v1 Announce Type: new Abstract: Autonomous vehicles (AVs) couple safety-critical perception to thermally constrained compute. Interfaces built for night perception and package thermal reliability were never engineered as confidentiality boundaries, and closing one such path leaves the other intact. Physical adversaries can spoof thermal infrared (IR) sensors used for night driving, while unprivileged software co-tenants can fingerprint perception workloads from thermal, dynamic voltage and frequency scaling (DVFS), and thermal event interrupt (TEI) telemetry required for package safety. These paths differ in mechanism and privilege; both exploit interfaces that AV stacks cannot disable without compromising Safety of the Intended Functionality (SOTIF). Classical microarchitectural and cyber-physical systems (CPS) surveys address cache timing, adversarial machine learning, and camera or LiDAR spoofing; no prior survey jointly frames physical thermal IR attacks and

arXiv:2610.20877v1 Announce Type: new Abstract: Early detection of mild cognitive impairment remains a critical bottleneck in dementia care, demanding scalable tools capable of capturing subtle behavioral decay. To capture the subtle temporal changes of cognitive decline, we introduce a behavioral dataset collected across seven unscripted multimodal interactions over an 18-month horizon. This dataset formalizes continuous, multi-session interactions into a dense 4D tensor (Batch * Session * Time * Feature) that explicitly preserves concurrent local dynamics alongside long-term changes. To effectively process these complex behavioral patterns, we propose CogDrift-4D: a novel, hierarchical 4D Transformer framework. To capture the complex trajectory of cognitive decline, CogDrift-4D implements a dual-level behavioral fatigue and drift modeling architecture. At the intra-session level, the model computes localized least-squares regression slopes to quantify the acute ``fading'' of

arXiv:2610.20661v1 Announce Type: new Abstract: Big data analytics supports modern intelligent systems, but model complexity and limited transparency can undermine interpretability, trust, and security in decision-making. This paper presents the Hybrid DeepXAI Fusion Network, a deep learning framework that integrates temporal, relational, and contextual features with explainable artificial intelligence (XAI) mechanisms. The architecture combines one-dimensional convolutional neural networks (1D-CNNs) for temporal feature extraction, Transformer encoders for contextual modeling, and graph neural networks (GNNs) for inter-feature dependency analysis. An attention-based fusion mechanism integrates these representations. Class Activation Mapping (CAM) and an explanation loss are incorporated to improve interpretability and explanation consistency. The framework is designed for deployment in a federated cloud architecture, using homomorphic encryption and blockchain-based auditing to

Space-based solar power has the potential to meet Earth's energy needs many times over. Will it see the light of day?

Germany Ties Fusion Money to Milestones Germany will pay fusion startups only as they prove progress, and it

arXiv:2610.17407v2 Announce Type: replace Abstract: Generating realistic time-series requires faithfully reproducing rare but high-impact extreme events. Existing approaches often rely on specialized representations or extreme-focused training objectives, which may restrict the diversity of captured extremes or compromise overall distributional fidelity. We propose Cross-View Time-Frequency Diffusion (CrossTF), a framework that exploits discrepancies between time- and frequency-view denoisers for extreme-aware time-series generation. Our key empirical finding is extreme-sensitive cross-view asymmetry: moving along the direction connecting the two views' clean-sample estimates substantially changes extreme fidelity while having limited effects on overall generation quality. We further identify a late-stage cross-view window in which this discrepancy stabilizes and cross-view adjustments have a particularly strong effect on extreme fidelity. These findings motivate a staged optimization

arXiv:2610.20209v1 Announce Type: new Abstract: Routability prediction enables rapid evaluation of placement quality without routing, providing effective feedback for routability-driven placement optimization. Recent machine learning methods have achieved efficient routability prediction through data-driven approach. However, their prediction performance highly depends on the capability of input representations to capture essential circuit information. To address the limitations of existing circuit representations in simultaneously preserving logical topology, physical layout and routing resource information, this paper proposes HGRP, a heterogeneous graph neural network for routability prediction. HGRP models the cells, nets, and routing grids as distinct types of nodes and explicitly represents topological connectivity, spatial mapping, and grid adjacency, thereby constructing a heterogeneous graph representation that preserves multi-level circuit semantics. Based on the

arXiv:2610.14151v1 Announce Type: cross Abstract: Reliable probability estimates are important for the clinical interpretation of machine-learning classifiers, yet radiomics--image fusion models for renal cell carcinoma (RCC) are commonly evaluated primarily by discrimination. Using 396 patients from the KiTS23 dataset, we evaluated whether adding radiomics to four image-model families improved probabilistic performance in addition to discrimination. Seventeen individual models and four probability-average ensembles were assessed using discrimination, probability calibration, and proper scoring measures. Across 12 matched image-only versus fusion comparisons, AUC and AP improved in all 12, while Brier score and negative log likelihood (NLL) improved in 11 of 12 and expected calibration error (ECE) in eight of 12. The 3D MedVAE gated-fusion model achieved the strongest overall individual-model profile. Probability averaging across concatenation, cross-attention, and gated fusion

arXiv:2610.13221v1 Announce Type: cross Abstract: Cancer diagnosis and treatment increasingly rely on multi-modal diagnostic technologies that generate high-dimensional datasets. Although these datasets are rich in pathological information, they pose considerable challenges in interpretation, dimensionality reduction, and clinical applicability. Conventional machine learning approaches often fail to extract meaningful cross-modal features, thereby limiting predictive accuracy in patient risk stratification. To address these limitations, this paper proposes an unsupervised machine learning framework that integrates feature extraction, tensor decomposition, and clustering to improve cancer risk prediction. The framework employs tensor decomposition to fuse heterogeneous genomic and clinical data, enabling the extraction of latent, biologically meaningful features that outperform unimodal feature sets. Multiple feature extraction strategies are investigated, and the resulting features

arXiv:2610.13428v1 Announce Type: new Abstract: Self-consistent numerical simulations of free boundary instabilities, such as resistive wall modes (RWMs) and external kink modes, require special attention given their relevance on the path towards magnetic fusion based nuclear reactors. In this work, a new implementation and a verification benchmark are presented for the nonlinear three-dimensional (3D) full magneto-hydrodynamics (MHD) code SpeCyl, which self-consistently combines a free flow 3D treatment for the edge plasma velocity with double resistive wall thin shell magnetic boundary conditions, capable of capturing the essential features of common setups in experimental devices. Two coaxial resistive shells surround the plasma. Vacuum conditions are assumed between and outside the resistive shells, whose relative positions and time constants can be varied on purpose. The implementation is extensively verified against Shafranov's analytic theory, valid for a circular cross

arXiv:2610.12585v1 Announce Type: new Abstract: Semi-Lagrangian (SL) schemes advect a distribution function along characteristics and remap it to a fixed grid. They are unconditionally stable without a global solve, and with discontinuous Galerkin (DG) integration the transfer along a magnetic field line conserves mass exactly. This paper presents the parallel and GPU design of a 3X+2V SLDG drift-kinetic tokamak solver on unstructured, flux-surface-aligned poloidal planes of shaped cross section, extruded toroidally. Transport follows magnetic field lines across the planes, and each velocity channel advances a fixed distance per step, proportional to its parallel speed, so the halo depth a channel needs grows with its speed. Velocity-dependent ragged halos exploit this, sending each channel only its own upwind depth. The 4D decomposition includes the toroidal angle, the 2D poloidal plane and the magnetic moment, and leaves the parallel velocity whole: no stage transports across the

arXiv:2610.13808v1 Announce Type: cross Abstract: Variational quantum circuits (VQCs) are highly sensitive to circuit architecture, yet most quantum machine learning pipelines select a single best design and discard the remaining candidates. This paper addresses whether the structural diversity across quantum circuit search spaces can be used to produce stronger predictions through model fusion. We apply Combinatorial Fusion Analysis (CFA), a model fusion framework that selects and combines multiple scoring systems using rank-score function (RSF) and cognitive diversity (CD), to a pool of 29 architecturally diverse classical models and quantum circuits. Using a diversity-aware model selection method, we identify subsets of maximally diverse models and combine them with both score and rank combination. Experiments on four binary classification benchmarks covering different domains demonstrate that CFA-based hybrid classical-quantum fusion consistently outperform the best individual

arXiv:2610.13364v1 Announce Type: new Abstract: In navigating the challenges of computational and communication overhead within the 6G-era Collaborative Inference (CI), this paper explores the feasibility of employing Stacked Intelligent Metasurface (SIM) to perform CI, termed SIM-CI. The SIM functions as a physical computing unit, receiving multi-user Over-The-Air (OTA) aggregated features within the same spectrum. It thus executes the necessary spatial-frequency combining and inference in a single physical wave propagation.SIM-CI also incorporates a tri-heterogeneity robustness adapter to address the network heterogeneity, such as multi-modal data and user inference models, as well as a quality-aware contrastive approach that align features from different modalities and extraction levels into a unified feature space. Considering the heterogeneous and dynamic channels, we further integrate Channel State Information (CSI) as an additional input into the adapter design, enabling users'

arXiv:2610.13298v1 Announce Type: new Abstract: Multi-source semantic information fusion enables edge networks to support global inference using observations collected by distributed terminals. However, limited communication and computation resources restrict semantic uploading, leaving the edge server with incomplete tokens that differ in modality, semantic granularity, arrival time, and channel condition. The value of these tokens also depends on the complementary or redundant information provided by other terminals. In this paper, we propose a multi-source and multi-dimensional semantic information fusion framework that coordinates terminal uploading and edge-side fusion under resource constraints. We develop Hyper-Slot to combine semantic content with available token attributes through iterative token-to-slot assignment and slot-to-token feedback, producing a compact representation for global inference. To encourage useful semantic uploading, we further design a contribution-based

arXiv:2610.13221v1 Announce Type: new Abstract: Cancer diagnosis and treatment increasingly rely on multi-modal diagnostic technologies that generate high-dimensional datasets. Although these datasets are rich in pathological information, they pose considerable challenges in interpretation, dimensionality reduction, and clinical applicability. Conventional machine learning approaches often fail to extract meaningful cross-modal features, thereby limiting predictive accuracy in patient risk stratification. To address these limitations, this paper proposes an unsupervised machine learning framework that integrates feature extraction, tensor decomposition, and clustering to improve cancer risk prediction. The framework employs tensor decomposition to fuse heterogeneous genomic and clinical data, enabling the extraction of latent, biologically meaningful features that outperform unimodal feature sets. Multiple feature extraction strategies are investigated, and the resulting features are

Princeton Plasma Physics Laboratory may have found a dramatically more efficient route to fusion energy by reversing the traditional approach: heating plasma first, then increasing its density. Their calculations could help future reactors reach self-sustaining fusion with far less energy while avoiding hidden problems that make ignition harder.

arXiv:2610.11890v1 Announce Type: new Abstract: The LM26 device has achieved a peak electron temperature of $T_e = 1180 \pm 65$ eV as measured by filtered X-ray diodes, supported by adjacent Thomson scattering measurements of $T_e = 1090 \pm 40$ eV, with a deuterium ion temperature of $T_i = 459 \pm 33$ eV as inferred from neutron yield. LM26 compresses a spherical tokamak plasma inside an initially 1.76 m diameter solid lithium liner imploded by theta-pinch coils. Radial compression by a factor of 2.75 increases $T_e$ over fivefold, while $T_i$ increases as much as twofold. The integrated observation of magnetic flux compression, electron heating, ion heating, and neutron production in a magnetized plasma compressed by a large lithium liner is an important milestone for magnetized target fusion

arXiv:2610.11871v1 Announce Type: new Abstract: Long pulse operation in magnetic fusion devices requires well controlled plasma power exhaust to the divertor \& wall, and avoidance of wall hot spots that could evolve in wall damage. At WEST, 10 major plasma facing components are monitored using 10 series of temperature/power indicators, based on multiple diagnostic systems, among which the infrared viewing system is especially relevant. These indicators span from the most basic ones (temperatures, power and energy from deterministic models) to advanced processes using artificial intelligence acquired through machine learning. Some advanced processes do operate in real time, and feedback on power actuators through the plasma control system, providing active control toward remaining within the safe operational domain. Other advanced processes intervene as forensic tools post discharge to identify possible dangerous situations regarding the power loading to the wall, so that the

arXiv:2610.11769v1 Announce Type: new Abstract: LM26 is a magnetized target fusion experiment built to demonstrate compressional heating of a plasma with a collapsing solid lithium liner. We report the design of a Thomson scattering diagnostic for LM26 and measurements from the LMC-12 compression shot where an electron temperature of $1090 \pm 40 \rm{eV}$ was measured with high confidence. The LM26 Thomson scattering diagnostic can measure temperature during compression at 8 times and 2 spatial points. Access limitations restricted the design to narrow f/30 views, and a forward scattering configuration with scattering angles 10^{20}\rm{m}^{-3}$. Measuring at a small scattering angle significantly narrows the Thomson spectral broadening of the laser line. The narrow spectrum (1030-1063 nm) is advantageous for the design of optical

arXiv:2610.11603v1 Announce Type: new Abstract: A novel diagnostic is proposed for active probing of doped inertial confinement fusion (ICF) spherical shells, enabling direct measurements of mix and burn dynamics at next-generation implosion facilities via resonant absorption imaging with an X-ray free-electron laser (XFEL). The charge-state-sensitive imaging of embedded dopants provides spatiotemporally resolved constraints on ionization, opacity, and material mix seeded by hydrodynamic instabilities in stagnated plasmas. Proof-of-principle experiments demonstrating resonant XFEL probing of hot spot in coated copper wires driven by an ultra-short relativistic laser pulse, have established the feasibility of this approach. Furthermore, atomic and radiation-hydrodynamic simulations, combined with synthetic resonant X-ray absorption imaging, extend the diagnostic concept to laser-driven direct-drive ICF shells with copper-doped ablators and radiation-driven indirect-drive shells with

arXiv:2610.11061v1 Announce Type: new Abstract: Filamentation instabilities are generally regarded as detrimental to fast-electron transport in fusion plasmas because they increase beam divergence and redistribute deposited energy. Here, we use two-dimensional particle-in-cell simulations coupled to a spin-dependent deuterium-tritium fusion module to investigate whether filamentation can instead transfer energy from counterstreaming electrons to prepolarized fusion ions. The simulations show that magnetic filaments, together with longitudinal inductive and transverse charge-separation electric fields, accelerate initially stationary deuterons and tritons to energies at which fusion reactions occur. Over the parameter range examined, the calculated neutron yield increases with the saturated magnetic-field energy. The model further predicts anisotropic, spin-resolved neutron emission. The direction of maximum neutron polarization is approximately perpendicular to the dominant

arXiv:2610.10914v1 Announce Type: new Abstract: Decisions that will shape future fusion facilities, including the production of waste, the management of tritium, the achievement of safety, and impacts on land, water, and local communities, are increasingly becoming sociotechnical in nature. For prior energy technologies, fission among them, such decisions were made top-down and met sustained public opposition. Research shows such opposition is rarely a deficit of public understanding and is instead rooted in place-specific environmental, health, sociocultural, and economic concerns and in distrust of how technologies are selected and sited. We argue that fusion developers have an opportunity to design differently, and we propose an ecology of participation: multiple, complementary modalities of engagement sustained over time, replacing the one-off public consultations now typical of large infrastructure projects. We synthesize human- and environment-centered design frameworks and

arXiv:2610.10914v1 Announce Type: cross Abstract: Decisions that will shape future fusion facilities, including the production of waste, the management of tritium, the achievement of safety, and impacts on land, water, and local communities, are increasingly becoming sociotechnical in nature. For prior energy technologies, fission among them, such decisions were made top-down and met sustained public opposition. Research shows such opposition is rarely a deficit of public understanding and is instead rooted in place-specific environmental, health, sociocultural, and economic concerns and in distrust of how technologies are selected and sited. We argue that fusion developers have an opportunity to design differently, and we propose an ecology of participation: multiple, complementary modalities of engagement sustained over time, replacing the one-off public consultations now typical of large infrastructure projects. We synthesize human- and environment-centered design frameworks and

arXiv:2610.12150v1 Announce Type: new Abstract: In many engineering design problems, the objective and constraints depend on the state: the solution of a PDE determined by the design parameters. We consider improving a design while holding selected state observables near trusted values, which we call state preservation constraints. Constrained Bayesian optimisation handles these with a learnt feasibility model, but struggles with this problem's highly anisotropic feasible set. Our central idea is to pre-compute the set of controls whose linearised constraint response stays within tolerance, thereby pulling back the state-space constraint into design space. This linearisation defines an ellipsoid from which we can efficiently draw a large number of well-spread candidates. The underlying linear response map is refined online, and the ellipsoid is rebuilt accordingly. We demonstrate the method end-to-end on our key application - Tokamak divertor optimisation under plasma-boundary

arXiv:2610.11683v1 Announce Type: new Abstract: Fusing a learned camera-noise fingerprint with an RGB appearance backbone is an appealing route to generator-independent deepfake detection, because the noise residual is grounded in image-formation physics rather than in the texture statistics of a particular generator. We test, on FaceForensics++, whether a Noiseprint++ residual channel carries information \emph{complementary} to an RGB Xception backbone for face-swap detection. A three-model ablation (RGB-only, residual-only, late-fusion) shows that fusion does not improve over RGB alone and that the residual branch alone is near chance. A seven-level bottleneck diagnostic localizes the cause: the noise maps do carry a discriminative signal, but it is statistical---carried by the per-sample first and second moments (mean, variance, energy) of the residual---and the per-sample \texttt{InstanceNorm} layer placed at the noise-branch input, following the TruFor template, standardizes

arXiv:2610.11526v1 Announce Type: new Abstract: Spiking neural networks (SNNs) offer an energy-efficient computing paradigm through sparse event-driven computation, showing great potential for efficient multimodal learning. However, applying SNNs to high-level multimodal tasks, such as image-text retrieval (ITR), remains challenging, since sparse spike representations make it difficult to capture semantic structures required for cross-modal alignment. Existing spiking ITR methods rely on local alignment and additional soft-label supervision during training, while lacking awareness of structural and multi-granularity relationships. To address these issues, we propose a Multi-head Spiking Graph Attention Network (\textbf{MSGAT}) for structural modeling and equip it with dynamic attention heads to capture complementary relational patterns and enable spike-driven graph reasoning and aggregation. However, within a two-branch multi-granularity fusion framework, the fine-grained spike

arXiv:2610.10994v1 Announce Type: new Abstract: Cooperative integrated sensing and communication (ISAC) can support shared situational awareness for unmanned aerial vehicles (UAVs), road vehicles, and industrial mobile robots such as automated guided vehicles (AGVs). Yet multipath can make nodes agree on a virtual target while useful weak echoes remain unexploited. This paper develops cooperative fusion that separates association confirmation, state refinement, and residual recovery. A cellular reference signal model accommodates arbitrary node counts, three-dimensional motion, monostatic and bistatic links, nuisance parameters, and correlated errors. We characterize report distinguishability under bounded bias and shared-reflector ambiguities that survive additional nodes. Minimax estimation yields directional weak-report refinement that preserves confirmed associations and limits bias-sensitive innovations; acquisition accounts for request and upload costs. Controlled experiments

arXiv:2610.09308v1 Announce Type: cross Abstract: REBCO coated conductors have been used in ultrahigh field magnets for condensed matter physics research, nuclear fusion, particle accelerators, and NMR applications. It is well known, however, that they are not mechanically strong against delamination due to their intrinsic layered structures. As a result, the delamination issue has become one of the major design challenges of REBCO magnet coils. As a part of the development of the 40 T all-superconducting magnet at the National High Magnetic Field Laboratory, USA, large-scale coils wound by two-in-hand REBCO conductor with turn-to-turn insulation was designed and tested at 4.2 K in an 11.4 T background magnetic field. We found REBCO degradations within the windings and at the crossover joints during the tests. The postmortem inspection found that the degradation was caused by delamination. The delamination appears on the surface of the conductor as bubbles which were subsequently

arXiv:2610.09674v1 Announce Type: new Abstract: Real-time equilibrium reconstruction is essential for tokamak plasma control. Data-driven surrogates are usually trained on high-quality labeled experimental data, but producing a large amount of labeled data sufficient for surrogate learning requires many expensive discharge shots, and few exist at the start of a new campaign or the operation of a new device. To reduce this cost, we propose to pre-train on simulated equilibria, generated at low cost by physics-based simulation, then fine-tune on limited real data. We evaluate under three settings with increasing difficulties, i.e., in-distribution, out-of-distribution (shape), and cross-campaign (temporal) splits, which mirror how reconstruction is actually deployed. Experimental results validate that synthetic pre-training is highly effective when few real labels are available: fine-tuning on only 1% of the full available real dataset cuts the per-sample normalized root-mean-square

arXiv:2610.09190v1 Announce Type: new Abstract: Plasma edge simulations with codes like EMC3-EIRENE are widely used to study fusion magnetic confinement devices. However, numerical errors can significantly impact the simulation solution and are often insufficiently characterized. These errors consist of the statistical error, bias, and time integration error, resulting from the finite sampling and tracking of Monte Carlo particles, and the discretization error, resulting from a finite grid resolution. This work presents the first comprehensive numerical error analysis of EMC3-EIRENE simulations in stellarator geometries, focusing on edge plasmas in the Helically Symmetric eXperiment (HSX). Fundamental plasma and neutral quantities are analyzed, and error reduction rates are compared against theoretical expectations. The statistical error, bias, and time integration error are fully characterized; however, quantification of the discretization error remains challenging because of the

arXiv:2609.07756v2 Announce Type: replace Abstract: Inertial confinement fusion (ICF) is a leading pathway toward clean energy, but each shot at the National Ignition Facility costs on the order of one million dollars, making accurate AI surrogates a high-value target. We study exogenous-driven ICF waveform prediction, where a 512-step neutron-rate diagnostic must be inferred directly from a laser pulse and target design parameters, with no historical response observed. The regime stresses standard time-series predictors with temporal sparsity (picosecond peak in a nanosecond window), input-output scale mismatch (under 300 real shots), and peak sensitivity (picosecond timing). We propose ICF-DLM, to our knowledge the first LM-based ICF predictor, combining (i) a physics-typed decomposition into yield $Y_{DT}$, peak timing $t_{\mathrm{peak}}$, and local waveform $w_{\mathrm{local}}$; (ii) bidirectional denoising that defers commitment to peak location; and (iii) a physics-driven PPO

arXiv:2610.08558v1 Announce Type: cross Abstract: Businesses regularly offer gift card programs to drive customer spending and increase engagement. A central question is how much incremental revenue these programs generate, and which channels drive it most efficiently. Measuring the incremental revenue associated with a gift card program is a challenging problem in causal inference, requiring a firm to infer how much each customer would have spent if they never received a gift card. Observational data on past customer purchasing behavior reveal possession of a gift card only when a customer makes a purchase, thus leaving a customer's treatment status systematically censored. In this paper, we develop a novel data fusion approach to overcome this missing data challenge. We identify and estimate incrementality by combining a large observational dataset with a smaller experimental dataset from a different population. Our approach relies on a mild transferability condition, which posits

arXiv:2610.07678v1 Announce Type: new Abstract: Accurate prediction of continuous molecular properties supports chemical discovery and design. Many recent approaches combine multiple molecular representations within increasingly complex neural architectures, aiming to achieve higher accuracy. However, classical machine-learning models remain competitive when trained on chemically meaningful molecular representations. We therefore constructed a chemistry-guided classical branch from Morgan fingerprints, RDKit molecular descriptors, and conformer-derived geometric features selected to provide complementary views of molecular structure. We combined four tree learners trained on these representations with a CoMPT graph predictor and integrated their out-of-fold predictions using Ridge regression. Across ESOL, FreeSolv, and Lipophilicity datasets, the fused model achieved lower mean root-mean-square error (RMSE) than Chemprop and competitive graph and multimodal methods, including CoMPT,

arXiv:2610.07658v1 Announce Type: new Abstract: This work develops a pressure-based coupled (PBC) GSIS. Within each GSIS iteration, a conventional kinetic solver advances the velocity distribution function, from which non-equilibrium constitutive relations are extracted. A PBC macroscopic solver then solves the steady macroscopic synthetic equations: continuity and momentum equations are solved as a coupled pressure-velocity system under fixed temperature, followed by a segregated temperature solve. A boundary treatment is constructed to maintain strict consistency between kinetic boundary fluxes and macroscopic boundary conditions throughout the macroscopic iterations. The proposed method is validated against direct simulation Monte Carlo (DSMC) data across multiple three-dimensional test cases, including tube-to-vacuum flows, a dynamic gas lock configuration for EUV lithography, divertors in nuclear fusion, and sphere flow at Mach numbers up to 10. Numerical results demonstrate that

arXiv:2610.07304v1 Announce Type: new Abstract: Analytical solutions to the three-dimensional ideal magnetohydrodynamic equilibrium equations $(\nabla \times \vec{B}) \times \vec{B} = \nabla p$ and $\nabla \cdot \vec{B} = 0$ provide benchmarks for stellarator equilibrium codes. Two families of non-axisymmetric solutions were recently discovered by Landreman [arXiv:2609.26742] as counterexamples to Grad's conjecture, but both satisfy stellarator symmetry. Here, we generalize both families by introducing a symmetry-breaking parameter into each of the solutions, resulting in new non-axisymmetric solutions that break stellarator symmetry and reduce to the stellarator symmetric solutions when the newly introduced parameter is set to zero.

arXiv:2610.08238v1 Announce Type: new Abstract: Awkward Array is a widely used library in high-energy physics (HEP) for representing and manipulating nested, variable-length data in Python. Previous CHEP contributions have explored GPU acceleration for Awkward Array, demonstrating the feasibility and performance benefits of CUDA-based backend while also identifying limitations related to irregular data access, fine-grained kernel launches, and composability of operations. In this contribution, we present recent developments that build directly on these earlier efforts by introducing a CUDA execution model for Awkward Array based on the Python CUDA Core Compute Libraries (CCCL). Using CCCL, we eliminate the need for custom CUDA kernels and can instead use a high-level Python interface. The CCCL-based approach also enables fusion of multiple Awkward operations into a reduced number of CUDA kernels, addressing kernel launch overhead observed in earlier GPU implementations. Lazy

arXiv:2610.06973v1 Announce Type: new Abstract: Melt-pool monitoring is central to qualifying metal additive manufacturing (AM), yet no public event-camera benchmark exists for this domain. Event cameras report per-pixel brightness changes with microsecond timing instead of reading full frames, giving the temporal resolution AM transients demand at a fraction of the data rate. We present SynAM-E (Synthetic AM Events), the first public multi-source simulated event-camera benchmark for metal-AM melt-pool monitoring: 85 physics-calibrated event shards from 15 sources across 8 institutions, with public baselines and fixed cross-machine evaluation splits. On a single-machine case study, event-spatial monitoring matches dense-frame accuracy (0.874 versus 0.863 macro-F1), and the absolute intensity that events discard adds only +0.006 under fusion. On the NIST Additive Manufacturing Metrology Testbed (AMMT) build, a near-sensor event-rate counter recovers a raw-frame-confirmed 528.7 Hz

arXiv:2610.06189v1 Announce Type: new Abstract: Tokamak disruptions caused by tearing modes can be prevented with magnetic feedback. A database of DIII-D locked mode disruptions provides criteria for major disruptions. The main criterion is that the q = 2 rational surface must be sufficiently close to the resistive wall surrounding the plasma to interact with it. This is shown in simulations of a sequence of model equilibria derived from the database. If the wall is ideally conducting, major disruptions do not occur. Magnetic feedback emulates a conducting wall, and can prevent major disruptions. Feedback coils must be designed appropriately, otherwise they are not effective in disruption prevention. Simulations of a model of MST also show the q = 2 criterion for major disruptions, as well as feedback stabilization. Feedback offers an attractive alternative to disruption mitigation by impurity injection.

arXiv:2610.06085v1 Announce Type: new Abstract: We present a computational design study of a stellarator-mirror hybrid concept, Stellarators Linking Axisymmetric Mirrors (SLAMs), in which an optimized poloidally omnigenous (OP or QI) stellarator serves as the rotational-transform-generating element that links long, axisymmetric (circular cross-section) magnetic mirrors. Starting from an OP precursor configuration obtained from the DESC omnigenity database, we construct hybrid coil sets for field period nFP = 2 SLAMs by separating the OP modular coils along the device midplane and inserting axisymmetric planar mirror coils into the resulting gap. Field-line tracing shows that nested vacuum flux surfaces survive this extrusion across a range of mirror-section lengths, with the cross-section qualitatively preserved at the point where the mirror is inserted. We use the field-line tracing data to map the shape of a surface and use the GVEC fixed-boundary solver to calculate an ideal

For more than 70 years, fusion energy researchers have used a particular equation to judge whether a plasma would stay hot and dense enough—for long enough—to reach a point where it could sustain itself without any more external power added. While that equation marks the finish line, also known as ignition, it says nothing about the best way to reach it. New research from the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) maps a path to ignition conditions using far less energy than any other path.

arXiv:2610.03433v1 Announce Type: cross Abstract: The susceptibility tensor of a homogeneous collisionless magnetized plasma possesses parity-time (PT) symmetry. For a stationary gyrotropic equilibrium, with the wave vector $\mathbf{k}$ and the equilibrium magnetic field $\mathbf{B}_0$ in the $x$--$z$ plane, the kinetic response satisfies $\chi(\omega^*,\mathbf{k})=R\chi(\omega,\mathbf{k})^*R$, where $R=\operatorname{diag}(1,-1,1)$. We show that the susceptibility PT-symmetry is a reduced-response-space manifestation of PT symmetry of the underpinning kinetic system. We identify parity $P$ and time reversal $T$ on the electromagnetic kinetic eigenmode space and show that the Hamiltonian operator $\mathcal{H}_{\mathrm{VM}}$ of the linearized Vlasov--Maxwell system is PT-symmetric, satisfying $PT\mathcal{H}_{\mathrm{VM}}(PT)^{-1}=\mathcal{H}_{\mathrm{VM}}$. The PT symmetry of the homogeneous magnetized Vlasov--Poisson eigenproblem is also established. Many kinetic instabilities

arXiv:2610.03433v1 Announce Type: new Abstract: The susceptibility tensor of a homogeneous collisionless magnetized plasma possesses parity-time (PT) symmetry. For a stationary gyrotropic equilibrium, with the wave vector $\mathbf{k}$ and the equilibrium magnetic field $\mathbf{B}_0$ in the $x$--$z$ plane, the kinetic response satisfies $\chi(\omega^*,\mathbf{k})=R\chi(\omega,\mathbf{k})^*R$, where $R=\operatorname{diag}(1,-1,1)$. We show that the susceptibility PT-symmetry is a reduced-response-space manifestation of PT symmetry of the underpinning kinetic system. We identify parity $P$ and time reversal $T$ on the electromagnetic kinetic eigenmode space and show that the Hamiltonian operator $\mathcal{H}_{\mathrm{VM}}$ of the linearized Vlasov--Maxwell system is PT-symmetric, satisfying $PT\mathcal{H}_{\mathrm{VM}}(PT)^{-1}=\mathcal{H}_{\mathrm{VM}}$. The PT symmetry of the homogeneous magnetized Vlasov--Poisson eigenproblem is also established. Many kinetic instabilities associated

arXiv:2610.02546v1 Announce Type: new Abstract: Fusion energy technology is rapidly approaching demonstrator deployment, but relatively little consideration has been given to publics' perceptions of or engagement with the technology. This research explores the perspectives of diverse professionals involved with the US fusion energy industry as to their knowledge on, valuation of, and preparedness for community and public (C&P) engagement on fusion energy. Participants perceived multifaceted value in C&P engagement from both industry and personal perspectives including an expressed desire for self-involvement in engagement activities, though ideally with increased support from home organizations and from engagement experts. Participants also revealed framings of fusion C&P engagement that align with deficit and contextual models of science communication thinking. These findings suggest an opportunity for collaboration between fusion professionals and engagement experts beyond one-way

arXiv:2610.02515v1 Announce Type: new Abstract: We introduce IGNITE, a generative world foundation model for fusion plasma behavior simulation trained in a self-supervised manner from over a decade of unlabeled experimental data at the DIII-D National Fusion Facility. The core of IGNITE is a dynamics model that can simulate DIII-D discharges from a given set of actuator trajectories. These trajectories can be supplied or generated on-the-fly from a textual prompt or from desired experimental outcomes. The model architecture consists of several spatio-temporal tokenizers that embed the different input modalities, including time-series like spatio-temporal measurement data, image sequences, and high-resolution spectrograms, each of which collected at vastly different time scales. The backbone is composed of an auto-regressive dynamics model that has the capacity to predict entire DIII-D discharges given initial latent plasma states and actuator trajectories over a theoretical infinite

arXiv:2610.02515v1 Announce Type: cross Abstract: We introduce IGNITE, a generative world foundation model for fusion plasma behavior simulation trained in a self-supervised manner from over a decade of unlabeled experimental data at the DIII-D National Fusion Facility. The core of IGNITE is a dynamics model that can simulate DIII-D discharges from a given set of actuator trajectories. These trajectories can be supplied or generated on-the-fly from a textual prompt or from desired experimental outcomes. The model architecture consists of several spatio-temporal tokenizers that embed the different input modalities, including time-series like spatio-temporal measurement data, image sequences, and high-resolution spectrograms, each of which collected at vastly different time scales. The backbone is composed of an auto-regressive dynamics model that has the capacity to predict entire DIII-D discharges given initial latent plasma states and actuator trajectories over a theoretical infinite

arXiv:2610.03505v1 Announce Type: new Abstract: Post-exposure microscopy is central to qualification of fusion materials. However, manual analysis does not scale to the volume, heterogeneity, and multiresolution character of modern fusion-materials campaigns. To address this challenge, we present a reproducible workflow, implemented in the Galaxy scientific workflow environment, for automated crack identification and quantitative damage assessment from scanning electron microscopy images. The workflow processes SEM images and experimental metadata to identify cracks, quantify damage, and retain the intermediate products and processing history needed for reproducibility. Outputs include crack masks, skeletonized crack networks, quality-control visualizations, and scalar damage descriptors. The method is designed to operate without image-specific parameter tuning across tungsten grades, microstructures, magnifications, and damage states. We demonstrate the workflow on a sparse

arXiv:2610.02752v1 Announce Type: new Abstract: Multi-scale design is crucial for efficient audio-visual speech separation, yet effectively modeling multi-scale information for audio-visual feature fusion remains challenging. We argue that the limited capacity of existing approaches primarily arises from: 1) treating features from different modalities in the same manner, and 2) overlooking the role of global features. To address these issues, we propose a Global-guided Asymmetric Attention Network (GAANet). Our model introduces two core innovations: first, an asymmetric multi-scale fusion framework that allows audio and visual streams to extract and interact with features at their respective optimal temporal resolutions, removing the need for symmetric temporal downsampling; second, a global-guided attention mechanism that compresses each modality into a compact global token with a temporal dimension of one, which then provides high-level semantic cues to guide both intra- and

arXiv:2610.00535v1 Announce Type: new Abstract: Uncertainty quantification is critical in scientific machine learning, where black-box, image-based models are increasingly deployed in high-stakes settings. In many such applications, model outputs inform costly decisions, yet most methods provide only point estimates without quantifying predictive uncertainty. This challenge is compounded by the limited accessibility and interpretability of model internals, making it difficult to assess reliability across different regions of the input space. As a result, there is a growing need for methods that can provide input-dependent uncertainty estimates to guide both model development and downstream experimentation. To address this need, we propose Adaptive Conformal Prediction using Nearest Neighbors (ACPNN), an input-adaptive conformal framework for image regression. ACPNN leverages information from neighboring samples to produce locally adaptive uncertainty estimates while maintaining low

arXiv:2610.00919v1 Announce Type: new Abstract: Magnetic field-line transport in magnetically confined plasmas is commonly modeled as diffusion through stochastic magnetic layers generated by resonant perturbations. However, experimentally relevant magnetic configurations frequently exhibit transport rates that are substantially slower than predicted by fully stochastic models, indicating the presence of unresolved dynamical barriers. In this work, we investigate the role of nested resonant island chains and their associated invariant manifolds in regulating transport within the edge magnetic topology of DIII-D tokamak discharge #171491 subjected to strong n=3 resonant magnetic perturbations. Using field-line tracing, Poincare analysis, and manifold calculations, we identify a hierarchy of homoclinic and heteroclinic tangles associated with both period-one and period-ten hyperbolic points. A systematic construction based on primary intersection points is developed to extract partial

arXiv:2610.00845v1 Announce Type: new Abstract: Machine learning models are increasingly used in feedback control loops for nuclear fusion, where inference speed and predictable timing are critical. We summarize lessons from models deployed for control on the DIII-D tokamak and develop a benchmark to compare inference backends across ten neural networks and model components from fusion control and diagnostic pipelines. For models greater than five million parameters, the CPU backends take tens to thousands of milliseconds, while GPU inference is substantially faster, suggesting an upper limit on CPU-oriented development for control. These results show why the deployment backend must be selected together with the model and its control-cycle budget.

arXiv:2610.00536v1 Announce Type: new Abstract: We present a fast, open-source calculation of the vacuum response matrix and surface inductance in non-axisymmetric geometries capable of running with up to 10^5 boundary points in tens of seconds on a laptop. Ideal magneto-hydrodynamic (MHD) stability modeling for free boundary modes requires the perturbed vacuum energy. We extend the VACUUM code to non-axisymmetric geometries in the new Julia implementation of the Generalized Perturbed Equilibrium Code, combining a collocation approach to the boundary integral equations with a high-order singularity correction scheme. The correction is recast into explicit quadrature weights acting on the collocation values, which allows the corrected operator to be assembled explicitly rather than applied to a given surface field, and the structure of that matrix under field-period and stellarator symmetry is then exploited to reduce storage and solve cost. We find that the vacuum response matrix

arXiv:2610.00244v1 Announce Type: new Abstract: Edge-localized modes (ELMs) are transient magnetohydrodynamic instabilities commonly observed in tokamak plasmas. Although deep time-series models can learn complex dynamic patterns from multi-diagnostic signals, their predictions are mainly data-driven and lack explicit utilization of ELM physics, plasma operating states, and historical discharge experience. As a result, they may confuse categories with similar temporal patterns, particularly under severe class imbalance. To address this issue, we propose KGF-ELM, a Knowledge Graph Fusion Network for ELM Recognition, and introduce a Knowledge Graph Collaborative Fusion Module (KGCFM) that organizes physical evidence from the current time window, ELM prototypes, relational knowledge graph representations, and similar historical discharge cases into complementary multi-source knowledge representations. KGCFM collaboratively models these knowledge sources through physics prototype

arXiv:2610.00243v1 Announce Type: new Abstract: Ion temperature Ti and electron temperature Te are fundamental kinetic parameters for evaluating tokamak plasma confinement, turbulent transport and magnetohydrodynamic stability. Reliable prediction of their spatiotemporal profiles is essential for long-pulse steady-state operation on the Experimental Advanced Superconducting Tokamak (EAST). However, existing EAST diagnostic systems have obvious measurement limitations, including narrow applicable conditions of charge exchange recombination spectroscopy and severe signal degradation of edge temperature diagnostics during long discharges. Classical physics-based transport codes suffer from high computational cost and error accumulation, while existing data-driven models only separately predict Ti or Te relying on spectral data, lacking joint Ti/Te modeling and failing under missing spectral measurements. In this work, we propose a multi-modal (i.e., global macroscopic diagnostic signals

arXiv:2610.00845v1 Announce Type: cross Abstract: Machine learning models are increasingly used in feedback control loops for nuclear fusion, where inference speed and predictable timing are critical. We summarize lessons from models deployed for control on the DIII-D tokamak and develop a benchmark to compare inference backends across ten neural networks and model components from fusion control and diagnostic pipelines. For models greater than five million parameters, the CPU backends take tens to thousands of milliseconds, while GPU inference is substantially faster, suggesting an upper limit on CPU-oriented development for control. These results show why the deployment backend must be selected together with the model and its control-cycle budget.

arXiv:2610.00535v1 Announce Type: cross Abstract: Uncertainty quantification is critical in scientific machine learning, where black-box, image-based models are increasingly deployed in high-stakes settings. In many such applications, model outputs inform costly decisions, yet most methods provide only point estimates without quantifying predictive uncertainty. This challenge is compounded by the limited accessibility and interpretability of model internals, making it difficult to assess reliability across different regions of the input space. As a result, there is a growing need for methods that can provide input-dependent uncertainty estimates to guide both model development and downstream experimentation. To address this need, we propose Adaptive Conformal Prediction using Nearest Neighbors (ACPNN), an input-adaptive conformal framework for image regression. ACPNN leverages information from neighboring samples to produce locally adaptive uncertainty estimates while maintaining low

arXiv:2609.39484v1 Announce Type: new Abstract: Longitudinal clinical cohorts are multimodal, irregularly sampled and pervasively incomplete: in ADNI, positron emission tomography and cerebrospinal fluid assays are absent from roughly half of all visits. Linear state-space models handle irregular sampling gracefully but treat a missing modality by masking the input, leaving the transition operator untouched. We prove that this is a representational limitation: the latent state of any linear state-space layer whose transition operator does not depend on the availability pattern is an additive function of the availability indicators, so no such layer can represent an interaction between two modalities being jointly present or jointly absent. We propose CAMOS, which gives each modality a bank of second-order oscillators coupled through a matrix that sits inside the differential equation and is gated by availability, so the transition operator itself becomes a function of which

arXiv:2609.39438v1 Announce Type: new Abstract: In integrated sensing and communication (ISAC) systems involving multiple operators, unmanned aerial vehicle (UAV) tracking can benefit from sensing at multiple base stations, while raw sensing records, channel information, and transmit decisions remain local to each operator. The resulting problem is to obtain a reliable UAV prediction from different track estimates and use it to guide transmission without centralizing their data or control variables. To resolve this, we propose a two-timescale framework comprising slow-timescale motion-uncertainty calibration and online track fusion with predictive beamforming. Local radar innovations calibrate the uncertainty assigned by each tracker to unmodeled UAV motion, with only the resulting parameters shared across operators. During online tracking, covariance intersection (CI) combines local estimates without requiring knowledge of correlations between their estimation errors. The fused

arXiv:2609.40132v1 Announce Type: new Abstract: Runaway electron (RE) beams generated during tokamak disruptions can deposit highly localized heat loads on plasma-facing components, posing a serious risk of melting and damage. Monte Carlo particle transport simulations coupled with three-dimensional thermomechanical response modeling can quantify this damage but are too computationally demanding for device-scale assessments and extensive scenario scans. We present FIREWALL (Fast Integrated Runaway Electron WALL loads), a surrogate model that combines a database of \textsc{Geant4} volumetric energy-deposition profiles with a one-dimensional heat-diffusion solver for each wall element. FIREWALL retains the energy and incident angle distributions of impacting REs and predicts the spatiotemporal temperature evolution of detailed three-dimensional wall geometries up to the melting threshold. FIREWALL thus provides a fast physics-based framework for translating global RE simulations into

arXiv:2609.39376v1 Announce Type: new Abstract: In a burning plasma, fusion-born alpha particles sustain the fusion reaction by heating the plasma core. Alpha-particle confinement in stellarators is sensitive to both deviations from omnigeneity and time-dependent Alfv\'enic perturbations. Traditional Poincar\'e maps rely on symmetry assumptions or dimensionality reductions that fail for stellarators with quasisymmetry (QS) error or multi-harmonic Alfv\'enic perturbations. To address these challenges, we investigate weighted Birkhoff averaging (WBA) as a diagnostic to detect the onset of chaos in guiding-centre trajectories subject to general magnetic perturbations. WBA exploits the super-convergence of time averages along quasiperiodic orbits, discriminating regular from chaotic trajectories using any smooth observable rather than an exact invariant of motion. Applying WBA to the canonical momentum $P_\eta$, we show that convergence tracks underlying orbit regularity even when

arXiv:2609.39484v1 Announce Type: cross Abstract: Longitudinal clinical cohorts are multimodal, irregularly sampled and pervasively incomplete: in ADNI, positron emission tomography and cerebrospinal fluid assays are absent from roughly half of all visits. Linear state-space models handle irregular sampling gracefully but treat a missing modality by masking the input, leaving the transition operator untouched. We prove that this is a representational limitation: the latent state of any linear state-space layer whose transition operator does not depend on the availability pattern is an additive function of the availability indicators, so no such layer can represent an interaction between two modalities being jointly present or jointly absent. We propose CAMOS, which gives each modality a bank of second-order oscillators coupled through a matrix that sits inside the differential equation and is gated by availability, so the transition operator itself becomes a function of which

arXiv:2609.39514v1 Announce Type: new Abstract: Vision-language-action (VLA) models bridge multimodal understanding and robotic control, advancing the dominant paradigm for embodied intelligence. However, most existing models rely on large Transformers, whose latency and energy costs hinder deployment on resource-constrained platforms. Through sparse event-driven computation, spiking neural networks offer a promising paradigm for high-performance and energy-efficient computing. Here, we propose the first Spike-driven VLA framework enabling end-to-end direct training for robotic manipulation, which mainly comprises three core components. First, we develop spiking visual and instruction encoders for multimodal perception, encoding visual observations and language instructions into sparse, reliable spike representations for subsequent cross-modal fusion. Then, we introduce Multi-Winner Spike Fusion for instruction-guided scene understanding, using bidirectional top-$k$ winner-take-all

arXiv:2609.39438v1 Announce Type: new Abstract: In integrated sensing and communication (ISAC) systems involving multiple operators, unmanned aerial vehicle (UAV) tracking can benefit from sensing at multiple base stations, while raw sensing records, channel information, and transmit decisions remain local to each operator. The resulting problem is to obtain a reliable UAV prediction from different track estimates and use it to guide transmission without centralizing their data or control variables. To resolve this, we propose a two-timescale framework comprising slow-timescale motion-uncertainty calibration and online track fusion with predictive beamforming. Local radar innovations calibrate the uncertainty assigned by each tracker to unmodeled UAV motion, with only the resulting parameters shared across operators. During online tracking, covariance intersection (CI) combines local estimates without requiring knowledge of correlations between their estimation errors. The fused

arXiv:2609.39048v1 Announce Type: new Abstract: Protein optimization remains a longstanding goal in life sciences. Existing machine learning-assisted directed evolution (MLDE) methods primarily rely on sequence-only features, overlooking the critical spatial constraints and co-evolutionary interactions encoded in protein structures. However, directly integrating structural information remains challenging due to the scarcity of reliable mutant structures. To address these issues, we propose StructEvo, a novel structure-aware reinforcement learning framework for protein directed evolution. StructEvo employs a delta-structure fusion encoder to approximate mutant structure features via feature differences, enabling dynamic incorporation of spatial knowledge. The vast mutation space is then decomposed into manageable subspaces through a structure-aligned hierarchical action network, while a geometric constraint further stabilizes delta feature learning. Our approach outperforms prior

arXiv:2609.39019v1 Announce Type: new Abstract: Multi-view learning seeks to learn more comprehensive representations by exploiting the complementarity and consistency across diverse modalities or views. However, existing multi-view fusion strategies treat intra- and inter-view fusion as independent stages, without simultaneously considering the evolution within views and the dependency across views. Such an asynchronous fusion paradigm inevitably constrains cross-view interactions due to conflicting view-specific structural inductive biases. As a result, information flow is prone to distortion and compression along intermediate pathways, confining the model to learn within a restricted solution space. To address this, we propose Synchronous Multi-view Neural Diffusion (SynMDiff), which conceptualizes the multi-view feature space as a unified dynamical system driven by a diffusion process. By modeling the diffusion flow across arbitrary dyadic feature interactions in a joint space,

arXiv:2609.38560v1 Announce Type: new Abstract: Mycosis fungoides (MF) is a rare form of cutaneous T-cell lymphoma that is often misdiagnosed in early stages due to its visual similarity to benign inflammatory dermatoses. Early and accurate diagnosis is critical for improving patient outcomes. In this paper, we propose a comprehensive diagnostic framework for automated MF detection that combines dual- scale histopathological image analysis with deep learning. To distinguish MF from other lymphoproliferative skin conditions, the proposed approach leverages a late-fusion ensemble of dual- magnification (10x and 20x) convolutional neural networks (CNNs), complemented by a random forest classifier trained on 16 clinical features. Experimental results on an expanded dataset of 6,267 images (4,306 MF; 1,961 Non-MF) across 463 patients demonstrate that strong detection performance is obtained by prioritizing higher-resolution cytological details (20x) within broader architectural context

arXiv:2609.37609v1 Announce Type: cross Abstract: Magnetohydrodynamics (MHD) is central to plasma modeling in astrophysics, space science, fusion, and engineering, but resolving multiscale MHD dynamics is computationally expensive. Machine-learning surrogates enable fast inference by learning reusable solution operators, yet existing models require separate training for each physical regime, limiting generalization across varying parameter settings. We introduce PHASE, a PHysics-Adaptive Scalable operator with residual Error correction, designed to model incompressible MHD across varying physical parameters with a single model. PHASE combines transfer learning, regime-aware adaptation, physics-centered learning, and residual refinement to improve both physical fidelity and generalization across MHD regimes. Together, these improvements achieve state-of-the-art prediction accuracy on two-dimensional MHD turbulence by reducing relative $L_2$ errors on physical fields by more than an

arXiv:2609.36713v1 Announce Type: new Abstract: Tritium is a naturally occurring radioactive isotope of hydrogen; a low-energy beta emitter with its health significance through internal exposure. The anticipated deployment of fusion energy systems is expected to increase tritium production significantly. Here we present an integrated assessment of tritium generation, release pathways, and resulting environmental concentrations across existing and emerging nuclear technologies. We first compile a comprehensive database of tritium production and releases from monitoring reports and literature. Analysis of light-water-reactor operating data shows that tritium discharge levels and release pathways are governed mainly by plant-specific strategies rather than reactor power or design parameters. This pattern reflects industry operating practices and efforts, with releases remaining well below regulatory limits. Our synthesis further indicates that FLiBe-based (Li2BeF4) systems may increase

arXiv:2609.36536v1 Announce Type: new Abstract: We study equilibrium reconstruction for the Korea Superconducting Tokamak Advanced Research (KSTAR) device under magnetic sensor dropout, with a diffusion model as the prior. Magnetic measurements leave most components of the toroidal current density $J_\phi$ undetermined, and sensor loss leaves more of them to the prior. The diffusion model learns only $J_\phi$, conditioned on the coil currents, the plasma current and the product of major radius and toroidal field, which do not depend on the dropout. The physics enters through a linear forward operator built from sensor response matrices of the LIUQE code. Because the observations are linear, a variant of decoupled annealing posterior sampling fits them with a closed-form linear correction. On measured signals of 37 KSTAR shots, we switch off a random fraction (0.0 to 0.9, ten settings) of the 124 magnetic channels in use and compare with LIUQE under the same masks, taking the

arXiv:2609.36528v1 Announce Type: new Abstract: In direct-drive inertial confinement fusion (ICF), cross-beam energy transfer (CBET) redirects a significant fraction of the incident laser energy out of the plasma. We report the experimental demonstration that broadband lasers reduce CBET-enhanced reflected-light return, performed at the low-coherence Kunwu laser facility with two crossed beams of 0.6% bandwidth and up to 550J at $\sim$2.6$\times$10$^{14}$ W/cm$^{2}$. A coupled ray-tracing model shows that the strong CBET amplification of narrowband reflected light is much weaker under broadband illumination. In symmetric incidence condition of two orthogonal beams, the total fractional scattered energy decreases from 7.57% to 4.64%; in asymmetric incidence condition, which separates stimulated Brillouin scattering (SBS) from specular reflection, the SBS fraction decreases from 3.75% to 0.46% and the specular-reflection fraction decreases from approximately 2.2% to 1.3%. These results

arXiv:2609.36299v1 Announce Type: new Abstract: Analytical studies of zonal-flow (ZF) excitation by fluctuating fields, such as drift-wave turbulence and Alfv\'en eigenmodes (AEs) driven by energetic particles (EPs) in tokamaks, are usually carried out via intricate gyrokinetic calculations and simulations whose results are notoriously difficult to interpret. Here, we report a transparent result that is also not limited to any particular modes or frequency range. Using oscillation-center theory that captures both ponderomotive forces and quasilinear diffusion, we derive a compact formula for the ZF drive produced by any fluctuating field in terms of the field's canonical momentum and the dissipation power density. For ZFs generated by drift waves, our model subsumes the local relation between the zonal velocity and the drift-wave energy density that was previously derived ad hoc. The simplicity and generality of our result also opens a path toward optimization of ZF excitation with

arXiv:2609.36183v1 Announce Type: new Abstract: Stellarator ideal magnetohydrodynamic (MHD) codes that assume nested flux surfaces such as \texttt{VMEC} and \texttt{DESC} solve the equilibrium problem by prescribing the total toroidal magnetic flux, plasma profiles and the last closed flux surface (LCFS), which analytically determines the unique field in vacuum, whereas at finite $\beta$ bifurcations and distinct equilibria sharing the same boundary have been reported in the literature. In this paper, we propose prescribing the Poincar\'e cross-section of the field at a single toroidal plane, and implement it in \texttt{DESC}, where the new condition enters only through the linear constraints and therefore costs no more than a fixed-LCFS solve. Fixing the geometry on one plane rather than on a full toroidal surface generally leaves more of the spectral coefficients free, and the ones it frees are those carrying the toroidal variation of the boundary flux surface, which a prescribed

arXiv:2609.36123v1 Announce Type: new Abstract: Loss-cone boundary determination in tokamaks is cast as an active learning problem in which a Bayesian logistic regression model with radial basis function features acts as the learner and the guiding-center orbit integrator acts as the labeling method. The approach is applied to an analytic tokamak equilibrium across a mesh of launch positions, producing a database of loss-probability models with approximate posterior uncertainty, each generated within a prescribed trajectory budget. Once generated, the database supports downstream applications, illustrated here with three examples. First, integrating the probability models against a Maxwellian distribution yields the lost-ion fraction and the energy and parallel momentum carried by lost ions, with posterior uncertainty propagated analytically to each integral. Second, a steady balance between the resulting orbit-loss torque and radial angular-momentum diffusion gives co-current edge

arXiv:2609.36754v1 Announce Type: cross Abstract: Explicit prosodic cues may help automatic speech recognition (ASR) of spontaneous speech, but auxiliary representations typically require additional trainable components, making it unclear whether gains come from the auxiliary information or the fusion mechanism. We address this using a frozen HuBERT backbone and a 64-dimensional representation trained to predict log F0, voicing, Delta log F0, log energy, and spectral tilt. We compare a frozen-backbone recognizer (Baseline), trainable fusion with zero auxiliary input (Null), and the same fusion supplied with the learned representation (Learned). Across Buckeye, Switchboard, and AMI IHM, Null reduces WER by 0.71-1.45 points over Baseline, whereas Learned differs from Null by +0.07, -0.09, and +0.00 points, with no significant differences. However, removing or mismatching the representation at inference increases Learned WER. Thus, Learned depends on the representation yet shows no

arXiv:2609.37609v1 Announce Type: new Abstract: Magnetohydrodynamics (MHD) is central to plasma modeling in astrophysics, space science, fusion, and engineering, but resolving multiscale MHD dynamics is computationally expensive. Machine-learning surrogates enable fast inference by learning reusable solution operators, yet existing models require separate training for each physical regime, limiting generalization across varying parameter settings. We introduce PHASE, a PHysics-Adaptive Scalable operator with residual Error correction, designed to model incompressible MHD across varying physical parameters with a single model. PHASE combines transfer learning, regime-aware adaptation, physics-centered learning, and residual refinement to improve both physical fidelity and generalization across MHD regimes. Together, these improvements achieve state-of-the-art prediction accuracy on two-dimensional MHD turbulence by reducing relative $L_2$ errors on physical fields by more than an order

arXiv:2609.37184v1 Announce Type: new Abstract: Dynamic simulations are an entrenched way of gaining insight into the evolution of system dynamics. Their computational cost however is often prohibitively high, especially in cases of stochastic frameworks. Machine learning algorithms are especially suited as simulation surrogates. Nevertheless, they face some very distinct limitations. Firstly, the sheer dimensionality of these systems, however, precludes the use of traditional time series models who struggle with high dimensional feature spaces. Additionally, traditional time series focus exclusively on either long or short range effects, causing local or global drift given enough time. In this paper, we propose a framework that addresses those limitations. Our framework combines a Variational Autoencoder, with a convolutional or graph basis that reduces the dimensionality of the system. This latent vector is propagated in time using a Temporal Fusion Transformer model, which includes

arXiv:2609.37162v1 Announce Type: new Abstract: RGB-T object tracking leverages the complementary characteristics of visible and thermal infrared modalities to improve robustness under adverse conditions. Existing supervised methods typically rely on costly modality-aligned bounding box annotations, while most self-supervised approaches follow a two-stage pseudo-labeling paradigm, making tracker training sensitive to pseudo-label quality and preventing joint end-to-end optimization. In this paper, we propose ESMTrack, a fully end-to-end self-supervised RGB-T tracking framework without offline pseudo-label generation or dense frame-level bounding box annotations. Given only the standard initial-frame annotation used in visual tracking, ESMTrack learns discriminative and temporally consistent representations through two complementary objectives: a grounding triplet loss on annotated initial frames and a cross-frame temporal triplet loss on unlabeled search frames, with reliable samples

arXiv:2609.37148v1 Announce Type: new Abstract: Many of the qualities that matter most in how people learn and grow, how someone regulates their emotions, reflects on a setback, or stays aware of others during a difficult conversation, are not directly observable. They have to be inferred from how someone speaks, moves, and sounds over time, and they resist the kind of clean labeling that most machine learning pipelines are built around. We study this challenge through a case that is well grounded in psychological theory but rarely modeled computationally: self-compassion, the tendency to respond to one's own setbacks with patience rather than harsh self-criticism. We examine how it appears during structured reflective interviews in a technology-mediated training setting, where people naturally talk through socio-emotionally demanding situations. Since no existing dataset captures this kind of construct in this kind of setting, we collected and annotated 51 reflective dialog sessions

arXiv:2609.33677v1 Announce Type: cross Abstract: In this work, a globally stiffly accurate Implicit-Explicit (IMEX) Runge-Kutta scheme is developed and implemented in the GBS code [Ricci et al., Plasma Phys. Control. Fusion, 2012], for two-fluid plasma turbulence simulations. The stiffest phenomena, governed by shear Alfv\'en waves and parallel diffusion, are treated implicitly, while the remaining non-stiff terms are advanced explicitly. This splitting enables time steps well beyond the Courant-Friedrichs-Lewy limit, without incurring the full computational cost of a globally implicit formulation. To efficiently solve the implicit subsystem at each time step, a three-dimensional physics-based preconditioner, inspired by techniques developed in the magnetohydrodynamic (MHD) context, is used. The resulting framework is verified through the method of manufactured solutions and exhibits both algorithmic and parallel scalability. Significant advantages in numerical stability and

arXiv:2609.34076v1 Announce Type: new Abstract: Isoflux control in spherical tokamaks requires coordinated plasma-current and boundary regulation under coil-voltage and computational constraints. This study develops observer-based constrained model predictive control (MPC) for EXL-50U. Vacuum-vessel spatial coarsening reduces a 538-state linearized model to 40 states while retaining control-relevant responses. A Kalman observer provides full-state estimates, and MPC jointly optimizes current and boundary-flux tracking with explicit voltage bounds. Matrix precomputation, specialized solver code generation, and structured linear-system solution reduce online computation. Nonlinear free-boundary Grad--Shafranov evolutive simulations evaluate nominal operation, measurement noise, configuration mismatch, a one-step delay, and combined noise and delay against proportional--integral--derivative (PID) control and a linear--quadratic regulator (LQR). Nominal MPC root-mean-square errors are

arXiv:2609.33994v1 Announce Type: new Abstract: High-bandwidth plasma diagnostics increasingly provide inputs to machine learning and signal-processing algorithms intended for real-time tokamak control, but the complete path from diagnostic sampling to control-system handoff is difficult to commission because of their sampling rates. We develop a packet-level digital hardware twin for the megahertz diagnostic edge-AI architecture. The simulator represents a 64-channel, 1 MHz beam emission spectroscopy (BES) diagnostic embedded in a 96-channel dual-carrier acquisition system, two 48-channel streams with SPAD0 sample counters, 10 GbE Hardware UDP transport, packet loss and network jitter, FPGA parsing and dual-carrier alignment, causal preprocessing and edge inference, a compact Ethernet result packet, receiver-side shared state, and a 1 kHz PCS-like control cycle. Binary UDP payloads and PCAP files are generated rather than emulating transport only at the array level. With a baseline

arXiv:2609.33677v1 Announce Type: new Abstract: In this work, a globally stiffly accurate Implicit-Explicit (IMEX) Runge-Kutta scheme is developed and implemented in the GBS code [Ricci et al., Plasma Phys. Control. Fusion, 2012], for two-fluid plasma turbulence simulations. The stiffest phenomena, governed by shear Alfv\'en waves and parallel diffusion, are treated implicitly, while the remaining non-stiff terms are advanced explicitly. This splitting enables time steps well beyond the Courant-Friedrichs-Lewy limit, without incurring the full computational cost of a globally implicit formulation. To efficiently solve the implicit subsystem at each time step, a three-dimensional physics-based preconditioner, inspired by techniques developed in the magnetohydrodynamic (MHD) context, is used. The resulting framework is verified through the method of manufactured solutions and exhibits both algorithmic and parallel scalability. Significant advantages in numerical stability and

arXiv:2609.33500v1 Announce Type: new Abstract: During plasma heating and current ramps the thermal and magnetic states of a tokamak evolve on different timescales. We present a rapid integrated tokamak modelling approach, which couples a variational fixed-boundary Grad--Shafranov equilibrium to sparse particle and energy flux matching and to weak-form current diffusion. An ITER-like pure-deuterium scenario is examined: a current ramp from 10 to 15~MA over 20~s, a 20~s flat-top and a 20~s ramp-down to 14~MA, under 80~MW of electron-cyclotron heating applied at the start. The toroidal current density at the pedestal top more than doubles during the ramp-up and then decreases during the flat-top as current redistributes inward. Thermal stored energy rises from 185.52 to 252.70~MJ and approaches its final value within the first few seconds. An independent calculation with the Fusion Synthesis Engine (FUSE) gives a stored-energy difference of 0.02\% at 60~s, whereas the largest profile

arXiv:2609.33341v1 Announce Type: new Abstract: The tokamak, a toroidal magnetic device confining a hot plasma, is one of the most advanced approaches to fusion energy. A central obstacle is turbulent transport driven by the ion-temperature-gradient (ITG) mode. Operation scenarios that rely on a weak or zero magnetic shear core, such as the ITER hybrid scenario, have achieved markedly improved confinement, yet the underlying mechanism has remained unclear. Here we show that the radial width of ITG poloidal harmonics is constrained not only by the familiar parallel Landau damping, but also by the isotropy of micro-turbulence in the plane perpendicular to the magnetic field. In the weak-shear limit, micro-isotropy dominates and constrains the radial width at one poloidal wavelength, yielding a critical magnetic shear $s_{\text{crit}} \approx 1/(2\pi)$. Above this threshold, the extended Type II ballooning modes prevail; below it, the localized Type I modes emerge, comprising only two or

arXiv:2609.31424v1 Announce Type: new Abstract: The edge radial electric ($E_r$) in the Tokamak \`a Configuration Variable (TCV) is compared in matched L-mode discharges with opposite ion magnetic drift directions (favorable versus unfavorable $B \! \times \! \nabla B$ configurations). As previously reported on the WEST and AUG tokamaks, the $E_r$ profile---measured by Doppler backscattering (DBS)---exhibits a "well" just inside the separatrix in the favorable drift case, which is absent or less pronounced in the unfavorable counterpart. This observation holds over a broad range of plasma conditions, notably also in Ohmic discharges with nearly identical edge density and temperature profiles. Density fluctuation characteristics inferred from DBS are not drastically different: Under Ohmic heating, edge fluctuation levels tend to be higher in the favorable configuration, while radial correlation lengths are similar. The edge $E_r$ difference appears uncorrelated with carbon toroidal

arXiv:2609.30452v1 Announce Type: new Abstract: Non-disruptive compass scan error field identification via island healing is demonstrated on KSTAR in a single H-mode discharge. This method can allow for up to a 75% reduction in the operational time necessary for a complete compass scan by reducing the required discharges from four to one. Previous experiments on DIII-D, JET, and MAST-U have demonstrated the technique in Ohmic and L-mode but required multiple discharges and strong real-time n=1 magnetics diagnostics close to the plasma that are unlikely to be viable for future devices. The results presented here were made possible by implementing a new set of triggering algorithms relevant to fusion pilot plant operation into the KSTAR plasma control system, along with KSTAR's strong RMPs and long pulse lengths. This represents a significant step towards the disruption-free error field identification method being demonstrated for deployment on future disruption-averse tokamaks,

arXiv:2609.31169v1 Announce Type: new Abstract: Multimodal Machine Translation aims to incorporate additional signal from non-textual modalities to improve translations by resolving ambiguities. While models, through multimodal fusion, are able to accept images related to the source text, they can ignore this information. Therefore, increasing their visual sensitivity remains an active research area. In this work, we introduce a training method, Metric-based Loss Weighting, that improves visual grounding of translations by increasing the loss function for tokens that benefit from the accompanying image. We identify these tokens using the Point-wise Cross-mutual Information (PCXMI) metric, which compares the model's output probabilities with and without visual context. We introduce a Congruency-based PCXMI metric and experimentally show that both metrics working in combination yield the best results. We evaluate our method by fine-tuning three pretrained Multimodal Large Language

arXiv:2609.30553v1 Announce Type: new Abstract: Expensive evolutionary search does not always need an exact fitness estimate for every candidate. It often needs a reliable answer to a simpler question: which candidate is better? We address this need through Teacher-Guided Learning NSGA-II (TGL-NSGA-II), a low-fidelity framework for constrained Tiny Machine Learning (TinyML) neural architecture search. A pretrained teacher organizes samples into strata defined jointly by difficulty and class. Each candidate then undergoes KD-Lite, a short and capped knowledge-distillation procedure on a compact training set, before being scored on a separate stratified evaluation set. This teacher-guided score is fused with a Gaussian-process surrogate to select candidates for full evaluation. For a fixed candidate population, we analyse evaluation variance, score concentration, pairwise rank inversion, expected Kendall-$\tau$, first-front identification, and hypervolume perturbation. We also derive a

arXiv:2609.30292v1 Announce Type: new Abstract: Online reviews shape consumer decisions, platform governance, and corporate reputation.Fake reviews compromise this information channel by injecting deceptive evidence into rating systems, recommendation pipelines, and public trust mechanisms.The rise of large language models, or LLMs, has changed the problem in two directions.LLMs can generate fluent and context-aware deceptive reviews, while pre-trained language models, or PLMs, and LLMs also provide stronger semantic representations for detection.This survey reviews fake review detection from an information fusion perspective, covering 211 studies published from 2018 to early 2026.We organize existing work by evidence source and fusion level, covering review text, sentiment, rating behavior, temporal metadata, user-product graphs, multimodal content, external knowledge, and LLM-generated signals.We trace the development from traditional machine learning and deep learning to PLM-based

Several companies have made significant headway toward nuclear fusion propulsion drives. Is humanity on the brink of a revolution in spaceflight?

arXiv:2609.29871v1 Announce Type: new Abstract: Slow magnetoacoustic pulsations (SMAPs) are found in MHD simulations of a tokamak plasma whose safety factor $q$ near the center is flat and slightly above unity ($q \gtrsim 1$). SMAPs are located on the central plateau of the slow magnetoacoustic continuum $\omega_{\rm S} = k_\parallel c_{\rm S}$, where $c_{\rm S}$ is the speed of sound and $k_\parallel$ the wavenumber parallel to the magnetic field. In our model, SMAPs exist when the ion viscosity and thermal diffusivity are sufficiently low. They can be driven unstable by a pressure gradient in the $q \sim 1$ region when the electric resistivity is sufficiently high. Exponentially growing SMAPs consist of standing slow waves on magnetic surfaces that are radially synchronized into a quasi-interchange structure with poloidal/toroidal mode numbers $m/n=1/1$. After free energy depletion, saturated quasi-linear pulsations (alternating $n=1$ and $0$) in the central $q\sim 1$ region couple

arXiv:2609.29849v1 Announce Type: new Abstract: Reliable correction of nonaxisymmetric error fields (EFs) is essential to the safety and performance of tokamak operation. Although resonant error field correction (EFC) is well established, supported by an improved understanding of 3D plasma response, the residual fields left after resonant EFC remain an open question: they are expected to be predominantly nonresonant, yet can still degrade both confinement and stability. Here we introduce a systematic EFC scheme that minimizes resonant and nonresonant EF effects simultaneously, based on neoclassical torque response computed from self-consistent perturbed equilibria. The scheme extends the method developed for designing quasi-symmetric magnetic perturbations to the case where actual error fields are present. Application to standard ITER target plasmas with a range of intrinsic EF scenarios demonstrates the advantages of this quasi-symmetric (QS) EFC scheme for controlling residual EFs.

arXiv:2609.29352v1 Announce Type: new Abstract: Integrated codes simulating interactions between Alfv\'{e}n waves and fast ions in tokamak plasmas use perturbative models for the relatively slow processes of instability growth, saturation, chirping and bursting, and transport. Faster processes by which an Alfv\'{e}n mode's spatiotemporal structure forms are assumed to have been completed within the mode's oscillation period, $\tau_0 \equiv 2\pi/\omega_0$. This separation of time scales underlies the computational efficiency of perturbative models, where the Alfv\'{e}n mode's time-dependence is reduced to that of a scalar signal $s(t) = A(t)\sin(-\omega_0 t - \phi(t))$ with variable amplitude $A(t)$ and phase $\phi(t)$. For this, accurate input data in the form of a mode's spatial structure $\delta\Phi({\mathbf x})$, damping rate $\gamma_{\rm d}$, and initial frequency $\omega_0$ are required. For modes residing in dense or continuous spectra, $\delta\Phi$ and $\gamma_{\rm d}$ could be

arXiv:2609.28730v1 Announce Type: new Abstract: Previous work built AI-based surrogates for a nonlinear gyrokinetic simulation code with the goal of using them for fast, direct calculations of turbulent ion heat flux in stellarator design optimizations and scenario planning for experiments. These AI surrogates were trained on data from >200k nonlinear, adiabatic electron gyrokinetic simulations with the gyrokinetic flux-tube code GX, using a wide range of stellarator magnetic configurations ($\sim$23k), positions in the plasma, and gradient scale lengths. In this paper, we demonstrate the use of the AI-based turbulence surrogate in the optimization of stellarator magnetic equilibrium and to speed up stellarator transport solvers. Due to its speed ($\sim$ms), the AI-based surrogate enables previously unattainable optimization objectives, such as full radial profiles of ion turbulent heat flux, or directly optimizing to maximize the turbulent critical gradient at multiple locations

arXiv:2609.29087v1 Announce Type: new Abstract: While deep learning accelerates expensive partial differential equation solving in computational fluid dynamics (CFD), existing methods like PINNs and FNOs often struggle with generalization, noise robustness, and physical consistency. We introduce the Transformer-Mamba for Flow Field (TM4FF) framework, a physics-constrained operator learning model with three key innovations: a Residual Wavelet Mamba (RWM) layer for feature denoising, a Transformer-based attention mechanism for enhanced feature fusion, and a physics-informed loss using Fourier derivatives to enforce the Navier-Stokes equations. Experiments on four CFD datasets show TM4FF achieves high accuracy and robust generalization across varying flow conditions.

arXiv:2609.27280v1 Announce Type: new Abstract: We study multitask regression when coefficient sharing can differ by predictor. For a given predictor, many tasks may have the same coefficient while a few differ, and the exceptional tasks need not be the same for another predictor. We describe this structure by two quantities: the number of active predictors and the total number of task coefficients that differ from the most common value for their predictor. We estimate the coefficient matrix by penalizing all pairwise coefficient differences across tasks, with an additional group penalty when predictor selection is needed. The resulting upper and lower bounds have the same dependence on these two quantities. We also consider the stronger setting in which a large set of tasks shares one entire coefficient vector. Under explicit sample-size conditions, the same pairwise estimator pools those tasks exactly, while allowing the remaining tasks to differ. Simulations and household energy

arXiv:2609.27940v1 Announce Type: cross Abstract: Starting from the cyclic duality tensors introduced by Vernier, Miao, and Yamazaki (VMY), whose endpoint-traced matrix-product operators obey $\mathbb Z_N$ Tambara--Yamagami fusion and realize topological defect lines of the compactified-boson conformal field theory, we retain the virtual endpoint as an $N$-state dynamical degree of freedom and construct an exactly movable seam in the spin-$\tfrac12$ XXZ chain at $q=\mathrm e^{\mathrm i\pi M/N}$ with $\gcd(M,N)=1$. The local movement identity holds for any unitary $q$-Weyl pair; in the finite cyclic realization, all output phases are locally gauge equivalent and share the same four seam eigenvalues. On a ring, the output gauge becomes a directed twist on a single bond, while endpoint conjugacy reduces the $2N$ labels to two relative $\mathbb Z_2$ flux classes. An explicit antiunitary symmetry protects the class $\eta\equiv M-1\pmod 2$: for even $N$ it pairs distinct charge sectors

arXiv:2609.28116v1 Announce Type: cross Abstract: Fast surrogates for tokamak boundary-plasma simulation are typically deterministic regressors mapping a global operating point to a flattened vector of cell values. Near the divertor detachment transition the steady state is not reliably single-valued. A point estimate must average over qualitatively different plasma states, and it arrives with no statement of confidence. Moreover, the flattened vector representation discards the geometric structure of the SOLPS-ITER mesh. This work addresses both problems. We unroll the curvilinear mesh into three fixed-size image tensors whose layout preserves cell adjacency and inverts exactly, letting a convolutional network act on the geometry without loss of information. A conditional flow matching model, well suited to highly sensitive systems, is then trained on this representation. The result is an efficient, scalable surrogate that captures multiple plausible outcomes even at sensitive