Published: 2026-03-23 14:25:00
Authors: Xiang-Pan Duan, Lin Chen, Guo-Liang Ma, Carlos A. Salgado, Bin Wu
Categories: hep-ph, hep-ex, nucl-ex, nucl-th
Abstract:
Motivated by color coherence and decoherence effects in the QCD medium, we propose a theoretical framework that combines vacuum-like emissions and medium-induced radiation to study jet quenching and its dependence on jet cone sizes and substructure. In our approach, a jet produced at a hard scale $Q$ first undergoes vacuum-like evolution, as described by the well-established generating-function method in the double logarithmic approximation. These vacuum-like emissions generate subjets at an infrared momentum scale $Q_0$. Each subjet then experiences medium-induced energy loss as described by the BDMPS-Z formalism. By modeling the QCD bulk medium using OSU (2+1)-dimensional viscous hydrodynamics and treating $Q_0$ together with the jet-quenching parameters at the initial proper time of the hydrodynamic evolution as free parameters, our approach provides a very good description of the inclusive jet modification factor $R_{AA}$ for large-radius jets and its dependence on jet substructure in 0-10% PbPb collisions at $\sqrt{s_{NN}} = 5.02~\rm{TeV}$, as measured by the ATLAS experiment.
Published: 2026-03-23 14:24:57
Authors: Jakob Wetzel, Javier Taboada-Gutiérrez, Matthias Roeper, Felix G. Kaps, Giuliano Esposito, Drini Marchese, Robin Buschbeck, Pauline Lenz, John M. Klopf, Hans A. Bechtel, Stephanie N. Gilbert Corder, Jeremie Teyssier, Susanne C. Kehr, Lukas M. Eng, Alexey B. Kuzmenko, Samuel D. Seddon
Categories: physics.optics, cond-mat.mtrl-sci
Abstract:
The control and steering of light at nanometre length scales is crucial for the development of both fundamental science and nanophotonic technologies. Recent advancements have been achieved by exploiting various crystalline anisotropies, allowing for subdiffractional and diffraction-less canalisation of energy. These studies in particular benefit from stacking and twisting of 2D materials, whereas corresponding capabilities of anisotropic bulk crystals are rather unexplored. In this work, we show that ferroelastic twin walls - crystallographically perfect 2D-sheets that separate regions of differently oriented domains - in the distorted perovskite LaAlO3 provide a natural platform for broadband lateral confinement and superb canalisation of light at the nanoscale. Without fabrication processes, the electromagnetic fields localised at such walls exhibit lateral optical sizes up to 260 times smaller than the free-space wavelength. Depending on the adjacent domain orientation and frequency, the twin wall pattern preferentially concentrates or repels the electromagnetic energy, constituting a natural building block towards broadband MIR and THz nanophotonics for polaritonic circuitry.
Published: 2026-03-23 14:18:07
Authors: Jian Ding, Cheng Wang, Hongju Li, Cheng Shu, Haifeng Yu
Categories: cs.CR, cs.IT
Abstract:
In Shamir's secret sharing scheme, all participants possess equal privileges. However, in many practical scenarios, it is often necessary to assign different levels of authority to different participants. To address this requirement, Hierarchical Secret Sharing (HSS) schemes were developed, which partitioned all participants into multiple subsets and assigned a distinct privilege level to each. Existing Chinese Remainder Theorem (CRT)-based HSS schemes benefit from flexible share sizes, but either exhibit security flaws or have an information rate less than $\frac{1}{2}$. In this work, we propose a disjunctive HSS scheme and a conjunctive HSS scheme by using the CRT for integer ring and one-way functions. Both schemes are asymptotically ideal and are proven to be secure.
Published: 2026-03-23 14:15:27
Authors: Alexey Gordeev
Categories: math.CO, cs.CG, math.MG
Abstract:
In 1992, Bollobás and Meir showed that for every $k \geq 1$ there exists a constant $c_k$ such that, for any $n$ points in the $k$-dimensional unit cube $[0, 1]^k$, one can find a tour $x_1, \dots, x_n$ through these $n$ points with $\sum_{i = 1}^n |x_i - x_{i + 1}|^k \leq c_k$, where $x_{n + 1} = x_1$ and $|x - y|$ is the Euclidean distance between $x$ and $y$. Remarkably, this bound does not depend on $n$, the number of points. They conjectured that the optimal constant is $c_k = 2 \cdot k^{k / 2}$ and showed that it cannot be taken lower than that. This conjecture was recently revised for $k = 3$ by Balogh, Clemen and Dumitrescu, who showed that $c_3 \geq 2^{7/2} > 2 \cdot 3^{3/2}$. It remains open for all $k > 2$, with the best known upper bound $c_k \leq 2.65^k \cdot k^{k / 2} \cdot (1 + o_k(1))$.
We significantly narrow the gap between lower and upper bounds on $c_k$, reducing it from exponential to linear. Specifically, we prove that $c_k \leq 2\mathrm{e}(k + 1) \cdot k^{k / 2}$ and $c_k = k^{k / 2} \cdot (2 + o_k(1))$, the latter establishing the conjecture asymptotically. We also obtain analogous results for related problems on Hamiltonian paths, spanning trees and perfect matchings in the unit cube. Our main tool is a new generalization of the ball packing argument used in earlier works.
Published: 2026-03-23 14:15:04
Authors: Giacomo Cristinelli, José A. Iglesias
Categories: math.OC
Abstract:
The Wasserstein-Fisher-Rao (WFR) distance on $S^{2}$ has recently been shown to coincide with a classical elastic distance between $S^{2}$-immersions in the theory of Riemannian shape analysis. While this correspondence holds in dimension $2$, the analogous statement fails in general on $S^{1}$ and, in the case of convex curves, it cannot be derived from existing two-dimensional arguments. In this paper, we establish that for convex absolutely continuous immersions of $S^{1}$ in the plane, the shape distance induced by the square root velocity transformation (SRVT) is indeed equivalent to the WFR distance acting on their associated length measures. The proof exploits a monotonicity principle for optimal transport on the universal cover of the circle, which in turn guarantees the existence of an optimal reparametrization achieving the SRVT infimum and enables a one-dimensional unbalanced optimal transport reformulation. Motivated by this equivalence, we further investigate the role of sparsity in shape optimization problems formulated in terms of length measures and regularized by the WFR distance. We study linear optimization over the corresponding balls, for which we prove a finiteness result when the reference measure is discrete, and propose a convex, positively one-homogeneous regularizer suitable for conditional gradient algorithms.
Published: 2026-03-23 14:12:48
Authors: Hubert Leterme, Andreas Tersenov, Jalal Fadili, Jean-Luc Starck
Categories: astro-ph.CO, astro-ph.IM, cs.LG, stat.ME
Abstract:
Upcoming stage-IV surveys such as Euclid and Rubin will deliver vast amounts of high-precision data, opening new opportunities to constrain cosmological models with unprecedented accuracy. A key step in this process is the reconstruction of the dark matter distribution from noisy weak lensing shear measurements.
Current deep learning-based mass mapping methods achieve high reconstruction accuracy, but either require retraining a model for each new observed sky region (limiting practicality) or rely on slow MCMC sampling. Efficient exploitation of future survey data therefore calls for a new method that is accurate, flexible, and fast at inference. In addition, uncertainty quantification with coverage guarantees is essential for reliable cosmological parameter estimation.
We introduce PnPMass, a plug-and-play approach for weak lensing mass mapping. The algorithm produces point estimates by alternating between a gradient descent step with a carefully chosen data fidelity term, and a denoising step implemented with a single deep learning model trained on simulated data corrupted by Gaussian white noise. We also propose a fast, sampling-free uncertainty quantification scheme based on moment networks, with calibrated error bars obtained through conformal prediction to ensure coverage guarantees. Finally, we benchmark PnPMass against both model-driven and data-driven mass mapping techniques.
PnPMass achieves performance close to that of state-of-the-art deep-learning methods while offering fast inference (converging in just a few iterations) and requiring only a single training phase, independently of the noise covariance of the observations. It therefore combines flexibility, efficiency, and reconstruction accuracy, while delivering tighter error bars than existing approaches, making it well suited for upcoming weak lensing surveys.
Published: 2026-03-23 14:06:46
Authors: Jianlin Chen, Gongyang Li, Zhijiang Zhang, Liang Chang, Dan Zeng
Categories: cs.CV
Abstract:
Transformer-based methods for RGB-D Salient Object Detection (SOD) have gained significant interest, owing to the transformer's exceptional capacity to capture long-range pixel dependencies. Nevertheless, current RGB-D SOD methods face challenges, such as the quadratic complexity of the attention mechanism and the limited local detail extraction. To overcome these limitations, we propose a novel Superpixel Token Enhancing Network (STENet), which introduces superpixels into cross-modal interaction. STENet follows the two-stream encoder-decoder structure. Its cores are two tailored superpixel-driven cross-modal interaction modules, responsible for global and local feature enhancement. Specifically, we update the superpixel generation method by expanding the neighborhood range of each superpixel, allowing for flexible transformation between pixels and superpixels. With the updated superpixel generation method, we first propose the Superpixel Attention Global Enhancing Module to model the global pixel-to-superpixel relationship rather than the traditional global pixel-to-pixel relationship, which can capture region-level information and reduce computational complexity. We also propose the Superpixel Attention Local Refining Module, which leverages pixel similarity within superpixels to filter out a subset of pixels (i.e., local pixels) and then performs feature enhancement on these local pixels, thereby capturing concerned local details. Furthermore, we fuse the globally and locally enhanced features along with the cross-scale features to achieve comprehensive feature representation. Experiments on seven RGB-D SOD datasets reveal that our STENet achieves competitive performance compared to state-of-the-art methods. The code and results of our method are available at https://github.com/Mark9010/STENet.
Published: 2026-03-23 14:04:01
Authors: Prajal Chettri, Shailesh Srivastava
Categories: physics.optics
Abstract:
Femtosecond laser micromachining (FLM) fabricated waveguides inherently form elliptical cores due to differences in focal spot size and the Rayleigh range of the microscope objective. Consequently, it is essential to study their propagation characteristics, which differ from those of conventional circular-core waveguides. In this work, we present the results of a parametric optimization of these waveguides to identify fabrication parameters that lead to minimal loss. A propagation loss characterization study revealed that, for a laser wavelength of 1030 nm, a pulse width of $\sim$300 fs, a pulse energy of 600 nJ, a scan speed of 2 mm/s, and a repetition rate of 100 kHz, a transparent and micro-bubble-free waveguide with a propagation loss of $\sim$0.4 dB/cm was formed. The modal analysis further demonstrated that the V-number depends on the core aspect ratio. The waveguide modes were compared with computationally generated modes, revealing a correlation that aligns well with existing literature.
Published: 2026-03-23 14:01:16
Authors: Adriano Meligrana
Categories: cs.SE, stat.CO
Abstract:
StreamSampling$.$jl is a Julia library designed to provide general and efficient methods for sampling from data streams in a single pass, even when the total number of items is unknown. In this paper, we describe the capabilities of the library and its advantages over traditional sampling procedures, such as maintaining a small, constant memory footprint and avoiding the need to fully materialize the stream in memory. Furthermore, we provide empirical benchmarks comparing online sampling methods against standard approaches, demonstrating performance and memory improvements.
Published: 2026-03-23 13:56:18
Authors: Alexander N. Manashov, Leonid A. Shumilov
Categories: hep-th, cond-mat.str-el
Abstract:
We calculate the correction exponents in the chiral Heisenberg model in the $1/N$ expansion. These exponents are related to the slopes of $β$ functions at the phase transition point. We present the results at order $1/N^2$ and check that they agree with the results of the $ε$ expansion near $d = 4$. We find that one of the correction exponents diverges as $d \to 3$. We argue that the appearance of the pole is a rather general phenomenon and is associated with operator mixing involving the system of four-fermion operators. After analyzing the operator mixing structure, we propose a resummation procedure which modifies the exponents already at leading order. We also perform calculations directly in the three-dimensional model and find complete agreement with the resummed exponents.
Published: 2026-03-23 13:45:07
Authors: Stephan Grebien, Julian Gurs, Roman Schnabel, Mikhail Korobko
Categories: quant-ph, physics.ins-det, physics.optics
Abstract:
Quantum-correlated networks distribute quantum resources such as squeezed and entangled states. These states are central to modern quantum technology, including photonic quantum computing, quantum communications, non-destructive biological sensing and gravitational-wave detection. Even for squeezed states of light - the most robust quantum-correlated resource - loss-induced decoherence remains the dominant obstacle to strong quantum advantage in in large-scale interferometric and networked quantum systems. Common design assumption in these applications is treating mismatches between spatial modes as a small, incoherent loss. Here we show that this picture can fail: coherent spatial-mode mixing with higher-order spatial modes can produce an apparent loss exceeding 100% relative to the initial squeezing, a regime we term hyperloss.
We experimentally demonstrate hyperloss in a minimal two-node quantum network: with only 8% mode mismatch, a 5.8dB squeezed state is converted into an effectively thermal state with no quadrature squeezing, eliminating the quantum advantage. Because the effect is coherent, it is controllable: lost correlations can be recovered by tuning differential spatial-mode phases (e.g., Gouy-/propagation-phase). We demonstrate this recovery experimentally, not only eliminating the hyperloss, but even significantly suppressing the mode mismatch loss, with 15% geometric mismatch acting like only ~2.8% effective loss.
Hyperloss is a design-limiting mechanism for all quantum networks with squeezed light, from from photonic quantum processors to large-scale interferometers and distributed quantum-sensing networks. Our results provide a practical route to avoid hyperloss and turn mode mismatch into an explicit, phase-aware design parameter for future quantum technologies.
Published: 2026-03-23 13:44:52
Authors: Zhong-Chen Gao, Tianyi Zhang, Feifei Wang, Jingguo Hu, Peng Yan, Xiufeng Han
Categories: cond-mat.mes-hall
Abstract:
Elasticity has long been regarded as a property exclusive to material media. Here we uncover its hidden existence in the spin degree of freedom. We introduce spin elasticity-an intrinsic mechanism that governs recoverable deformation of spin morphology. This discovery reveals a previously unrecognized universality: elasticity operates in both matter and spin spaces, underpinning structural integrity across physical realms. By establishing the missing spin counterpart, this work completes the elastic picture and points toward a broader paradigm where elasticity transcends its conventional boundaries.
Published: 2026-03-23 13:32:49
Authors: Bikashkali Midya
Categories: quant-ph, cond-mat.str-el, math-ph
Abstract:
Theoretical analysis of a prototypical two-qubit effective non-Hermitian system characterized by asymmetric Heisenberg $XY$ interactions in the absence of external magnetic fields demonstrates that maximal bipartite entanglement and quantum phase transitions can be induced exclusively through non-Hermiticity. At thermal equilibrium as $T\rightarrow 0$, the system attains maximal entanglement ${C}=1$ for values of the non-Hermiticity parameter greater than a critical value $γ>γ_c=J\sqrt{(1-δ^2)}$, where $J$ denotes the exchange interaction and $δ$ represents the anisotropy of the system; conversely, for $γ< γ_c$, entanglement is nonmaximal and given by ${C} = \sqrt{(1 - (γ/J)^2)}$. The entanglement undergoes a discontinuous transition to zero precisely at $γ= γ_c$. This phase transition originates from the closing of the energy gap at a non-Hermiticity-driven ground state degeneracy, which is fundamentally different from an exceptional point. This work suggests the use of singular-value-decomposition generalized density matrix for the computation of entanglement in bi-orthogonal systems.
Published: 2026-03-23 13:15:22
Authors: Junhao Du, Jialong Xue, Anqi Li, Jincheng Dai, Guo Lu
Categories: cs.CV
Abstract:
Video large language models (Video-LLMs) face high computational costs due to large volumes of visual tokens. Existing token compression methods typically adopt a two-stage spatiotemporal compression strategy, relying on stage-specific metrics and an implicit assumption of spatiotemporal separability. Under extremely low retention ratios, however, such approaches often result in unbalanced allocation and loss of visual evidence essential for question answering. We reformulate token compression as a spatiotemporal allocation task within a global token retention pool. We propose a unified selection mechanism that integrates attention weights and semantic similarity to globally select tokens with high contribution and low redundancy. Unselected tokens are merged via clustering and refilled, preserving information integrity. Inside the LLM, we further introduce text-aware merging to perform secondary compression based on query relevance. Without requiring retraining, our method serves as a plug-and-play module compatible with existing Video-LLMs. Experiments show that retaining only about 2% of visual tokens preserves 90.1% of baseline performance across multiple benchmarks, while reducing FLOPs to roughly 2.6%. These benefits generalize across diverse backbones, decreasing end-to-end inference latency and memory consumption. Our unified spatiotemporal token compression strategy establishes the state-of-the-art in video understanding under ultra-low token retention.
Published: 2026-03-23 13:09:00
Authors: Javier De Miguel, Enrique Joven, Elvio Hernández-Suárez, Juan F. Hernández-Cabrera, Haroldo Lorenzo-Hernández, Dylan Carroll, Roger J. Hoyland, Edgar S. Carlin, Antonios Gardikiotis, Abaz Kryemadhi, J. Daniel Marrero-Falcón, Marios Moroudas, Chiko Otani, J. Alberto Rubiño-Martín, Konstantin Zioutas
Categories: hep-ex
Abstract:
We report a pilot dark-matter search with a cryogenic, magnetized, scaled-down DALI prototype. An analysis of 36 hours of data reveals no statistically significant excess attributable to axionlike particles. We therefore set new exclusion limits in the 6.883--6.920 GHz band, reaching an axion-photon coupling sensitivity of $g_{aγγ}\lesssim 1.27\times10^{-11}\,\mathrm{GeV}^{-1}$ at 28.54 $μ$eV. These results consolidate the DALI approach and motivate a next-stage haloscope to explore a broader mass range with upgraded instrumentation.
Published: 2026-03-23 13:07:48
Authors: Chengzhi Li, Heyan Huang, Ping Jian, Yanghao Zhou
Categories: cs.MM
Abstract:
Audio-Visual Semantic Segmentation (AVSS) aligns audio and video at the pixel level but requires costly per-frame annotations. We introduce Weakly Supervised Audio-Visual Semantic Segmentation (WSAVSS), which uses only video-level labels to generate per-frame semantic masks of sounding objects. We decompose WSAVSS into looking, listening, and segmentation, and propose Progressive Cross-modal Alignment for Semantics (PCAS) with two modules: *Looking-before-Listening* and *Listening-before-Segmentation*. PCAS builds a classification task to train the audio-visual encoder using video labels, injects visual semantic prompts to enhance frame-level audio understanding, and then applies progressive contrastive alignment to map audio categories to image regions without mask annotations. Experiments show PCAS achieves state-of-the-art performance among weakly supervised methods on AVS and remains competitive with fully supervised baselines on AVSS, validating its effectiveness.
Published: 2026-03-23 12:47:47
Authors: Marvin Seegert, Christian Oefinger, Korbinian Moller, Christoph Bank, Johannes Betz
Categories: cs.RO
Abstract:
Proprietary Autonomous Driving Systems are typically evaluated through disengagements, unplanned manual interventions to alter vehicle behavior, as annually reported by the California Department of Motor Vehicles. However, the real-world capabilities of prototypical open-source Level 4 vehicles over substantial distances remain largely unexplored. This study evaluates a research vehicle running an Autoware-based software stack across 236 km of mixed traffic. By classifying 30 disengagements across 26 rides with a novel five-level criticality framework, we observed a spatial disengagement rate of 0.127 1/km. Interventions predominantly occurred at lower speeds near static objects and traffic lights. Perception and Planning failures accounted for 40% and 26.7% of disengagements, respectively, largely due to object-tracking losses and operational deadlocks caused by parked vehicles. Frequent, unnecessary interventions highlighted a lack of trust on the part of the safety driver. These results show that while open-source software enables extensive operations, disengagement analysis is vital for uncovering robustness issues missed by standard metrics.
Published: 2026-03-23 12:34:18
Authors: Seungyeop Lee, Jong-Han Kim
Categories: cs.RO, eess.SY, math.OC
Abstract:
In structured multi-agent transportation systems, agents often must follow predefined routes, making spatial rerouting undesirable or impossible. This paper addresses route-constrained multi-agent coordination by optimizing waypoint passage times while preserving each agent's assigned waypoint order and nominal route assignment. A differentiable surrogate trajectory model maps waypoint timings to smooth position profiles and captures first-order tracking lag, enabling pairwise safety to be encoded through distance-based penalties evaluated on a dense temporal grid spanning the mission horizon. The resulting nonlinear and nonconvex velocity-scheduling problem is solved using an inexact-projection Alternating Direction Method of Multipliers (ADMM) algorithm that combines structured timing updates with gradient-based collision-correction steps and avoids explicit integer sequencing variables. Numerical experiments on random-crossing, bottleneck, and graph-based network scenarios show that the proposed method computes feasible and time-efficient schedules across a range of congestion levels and yields shorter mission completion times than a representative hierarchical baseline in the tested bottleneck cases.
Published: 2026-03-23 12:30:58
Authors: Sufia Shahin, Mahdi Benkhelifa, Yogesh Singh Chauhan, Hussam Amrouch
Categories: cs.ET
Abstract:
In this article, we study the impact of self-heating effects (SHEs) and middle of line (MOL) and back-end of line (BEOL) induced parasitics on multi-tier CFET design, where multiple nanosheet devices are vertically stacked. We analyze and compare the 4-tier CFET design with the conventional 2-tier CFET, using TCAD models calibrated to experimental measurements. Additionally, TCAD simulations are used to model and analyze SHE-induced heat distribution and temperature profiles and to extract the detailed parasitic RC network from 3D models of CMOS inverters designed with full MOL and BEOL interconnects. At the device level, the maximum temperature rise (TMAX) caused by SHE in nFET and pFET devices of the 2-tier CFET architecture is 62 K and 74 K, respectively. Due to the increased distance from the substrate heat sink, the upper-tier nFET and pFET devices in the 4-tier design show higher TMAX of 83.5 K and 98.5 K and more heat trapping in the stacked layers. Furthermore, in the 4-tier CFET-based CMOS inverters, the BEOL-induced parasitic RCs are, respectively, 10 and 6.5 times higher in the top-tier than in the 2-tier CFET-based inverters. In the bottom tier, the corresponding parasitic RC elements are 6.26 and 2 times higher, respectively, than in the 2-tier inverters. Finally, compared to the 4-tier design without parasitics, the propagation delay of the top and bottom tier inverters increases by 10% and 8.2%, respectively, due to the interconnect parasitic RCs. For the conventional 2-tier inverter, the corresponding degradation of delay with parasitic RCs is 37.25%.
Published: 2026-03-23 12:27:46
Authors: Yichen Fan, Jacob Z. Williams, Weitao Yang
Categories: physics.chem-ph, cond-mat.mtrl-sci
Abstract:
Density functional theory (DFT) is the most promising method for calculating quantum properties of molecules and materials at moderate and large scales. However, commonly used density functional approximations (DFAs) have systematic delocalization error, as demonstrated by underestimated band gaps, over-delocalized charges, and energy level misalignment at interfaces, which limits its quantitative prediction. Extensive efforts, such as the $GW$ approximation to many-body perturbation theory, system-specific tuning of DFA parameters, and correction functionals have been developed to address delocalization error. However, an accurate, efficient, and unified solution to describe total energy, charge density and band structure for both finite systems and materials is still not available. Building on the linear-response localized orbital scaling correction (lrLOSC), we introduce olLOSC: a localized orbital scaling correction with curvature calculated by orbital-free electronic linear response. olLOSC has comparable accuracy to lrLOSC, but is much more computationally efficient. olLOSC corrects delocalization error - especially underestimated gaps, but also the total energy - both in molecules and in materials with small and moderate band gaps, within the same orbital-free approximation. Critically, with a a unified approximation, olLOSC opens the path for robust and efficient DFT applications across molecules, materials, and interfaces.
Published: 2026-03-23 12:25:56
Authors: Linkuan Zhou, Yinghao Xia, Yufei Shen, Xiangyu Li, Wenjie Du, Cong Cong, Leyi Wei, Ran Su, Qiangguo Jin
Categories: cs.CV, cs.AI
Abstract:
Unsupervised Domain Adaptation (UDA) is essential for deploying medical segmentation models across diverse clinical environments. Existing methods are fundamentally limited, suffering from semantically unaware feature alignment that results in poor distributional fidelity and from pseudo-label validation that disregards global anatomical constraints, thus failing to prevent the formation of globally implausible structures. To address these issues, we propose SHAPE (Structure-aware Hierarchical Unsupervised Domain Adaptation with Plausibility Evaluation), a framework that reframes adaptation towards global anatomical plausibility. Built on a DINOv3 foundation, its Hierarchical Feature Modulation (HFM) module first generates features with both high fidelity and class-awareness. This shifts the core challenge to robustly validating pseudo-labels. To augment conventional pixel-level validation, we introduce Hypergraph Plausibility Estimation (HPE), which leverages hypergraphs to assess the global anatomical plausibility that standard graphs cannot capture. This is complemented by Structural Anomaly Pruning (SAP) to purge remaining artifacts via cross-view stability. SHAPE significantly outperforms prior methods on cardiac and abdominal cross-modality benchmarks, achieving state-of-the-art average Dice scores of 90.08% (MRI->CT) and 78.51% (CT->MRI) on cardiac data, and 87.48% (MRI->CT) and 86.89% (CT->MRI) on abdominal data. The code is available at https://github.com/BioMedIA-repo/SHAPE.
Published: 2026-03-23 12:14:32
Authors: Xin Guo, Chunrui Zhao, Hong Jia, Ting Dang, Gongping Huang, Xianrui Zheng, Yan Gao
Categories: eess.AS
Abstract:
Integrating Federated Learning (FL) with self-supervised learning (SSL) enables privacy-preserving fine-tuning for speech tasks. However, federated environments exhibit significant heterogeneity: clients differ in computational capacity, causing straggler effects under unified fine-tuning, while diverse downstream tasks require different representation depths, making full-model updates inefficient. To address these challenges, we propose an adaptive federated fine-tuning framework with early exits. Lightweight prediction heads are inserted at intermediate layers of the SSL backbone, allowing clients to terminate computation based on local constraints and task requirements. We further introduce a layer-wise, depth-aware partial aggregation strategy to better utilize representations from different network depths. Experiments show that the framework reduces edge overhead, supports heterogeneous hardware, and maintains competitive performance in resource-constrained federated environments.
Published: 2026-03-23 12:09:37
Authors: Nikolas Stavrou, Siamak Mehrkanoon
Categories: cs.LG, cs.AI
Abstract:
Weather forecasting supports critical socioeconomic activities and complements environmental protection, yet operational Numerical Weather Prediction (NWP) systems remain computationally intensive, thus being inefficient for certain applications. Meanwhile, recent advances in deep data-driven models have demonstrated promising results in nowcasting tasks. This paper presents SmaAT-QMix-UNet, an enhanced variant of SmaAT-UNet that introduces two key innovations: a vector quantization (VQ) bottleneck at the encoder-decoder bridge, and mixed kernel depth-wise convolutions (MixConv) replacing selected encoder and decoder blocks. These enhancements both reduce the model's size and improve its nowcasting performance. We train and evaluate SmaAT-QMix-UNet on a Dutch radar precipitation dataset (2016-2019), predicting precipitation 30 minutes ahead. Three configurations are benchmarked: using only VQ, only MixConv, and the full SmaAT-QMix-UNet. Grad-CAM saliency maps highlight the regions influencing each nowcast, while a UMAP embedding of the codewords illustrates how the VQ layer clusters encoder outputs. The source code for SmaAT-QMix-UNet is publicly available on GitHub \footnote{\href{https://github.com/nstavr04/MasterThesisSnellius}{https://github.com/nstavr04/MasterThesisSnellius}}.
Published: 2026-03-23 12:05:37
Authors: Ian Crawford, Carl-Emil Pless
Categories: econ.GN
Abstract:
We study the associations between everyday economic decision-making quality and people's emotional states. Using high-frequency, highly disaggregated consumer "scanner" data, we show that the cost of poor decision-making is substantial, on average equal to around half of day-to-day consumption budgets. While material circumstances help explain decision-making quality, how people feel about those circumstances is equally important. Contrary to evidence that stress and worry impair performance in settings where distraction is costly, we find these same feelings are associated with improved decision-making for frequently made consumption choices. This is consistent with worry increasing attentiveness to decisions within households' locus of control.
Published: 2026-03-23 12:04:06
Authors: Aleksandra Urman, Anikó Hannák, Joachim Baumann
Categories: cs.IR, cs.SI
Abstract:
GoogleTrendArchive is a comprehensive archive of Google Trending Now data spanning over one year (from November 28, 2024 to January 3, 2026) across 125 countries and 1,358 locations. Unlike Google Trends, which requires specifying search terms in advance, Trending Now captures search queries experiencing real-time surges, offering a way to inductively discover trending patterns across regions for studying collective attention dynamics. However, Google does not provide historical access to this data beyond seven days. Our dataset addresses this gap by presenting an archive of Trending Now data. The dataset contains over 7.6 million trend episodes. Each record includes the trend identifier, search volume bucket, precise timestamps, duration, geographic location, and related query clusters. This dataset, among other, enables systematic studies of information diffusion patterns, cross-cultural attention dynamics, crisis responses, and the temporal evolution of collective information-seeking at a global scale. The comprehensive geographic coverage facilitates fine-grained cross-country or cross-regional comparative analyses.
Published: 2026-03-23 12:02:15
Authors: Tingkai Xue, Yu Jiao, Te Ba, Jingliang Wang, Juntao Yang, Simon See, Boyang Chen, Claire E. Heaney, Christopher C. Pain, Chang Wei Kang, Mohamed Arif Bin Mohamed, Hongying Li
Categories: physics.flu-dyn
Abstract:
In this work, we develop a neural-physics solver based on finite volume method (FVM), namely NeuralFVM, for turbulent flows by implementing the standard $k$-$ω$ model designed for efficient Graphics Processing Unit (GPU) execution. The governing equations for fluid flow and heat transfer are reformulated as local tensor operations using convolution-based stencil operators, which enables compatibility with deep learning libraries while preserving the conservative properties of the FVM. A key challenge in implementing the turbulent model within such a framework is the treatment of the stiff destruction terms in the $k$ and $ω$ transport equations. To address this issue, an operator-splitting strategy is introduced in which the stiff destruction terms are handled semi-implicitly while the remaining terms are advanced explicitly. This formulation avoids global matrix assembly and allows the entire solver to be implemented using local tensor operations. In addition, the pressure-velocity coupling is solved using a convolution-based geometric multigrid algorithm embedded within a neural network architecture. The resulting NeuralFVM solver is validated through comparison with simulations conducted using the commercial CFD software ANSYS Fluent for several channel-flow configurations and an indoor airflow scenario. The results demonstrate close agreement in velocity, temperature, and turbulence quantities, confirming the accuracy of the proposed approach. The developed GPU framework achieves a speedup of around 19-46 times compared with its Central Processing Unit (CPU) counterpart under different meshes. Moreover, the proposed solver naturally integrates with machine learning workflows, providing a promising foundation for future data-driven turbulence modeling and optimization.
Published: 2026-03-23 11:56:30
Authors: Zahra Sartipi, Richard Gundermann, Janet Anders, Peter Saalfrank
Categories: quant-ph
Abstract:
The canonically consistent quantum master equation (CCQME) method to treat system-bath dynamics is used to describe intramolecular proton transfer in the thioacetylacetone molecule (TAA, C$_4$H$_6$OS), modeled as an $N$-level quantum system coupled to a solvent. The solvent is represented as a harmonic bath (a continuum of oscillators) characterized by an Ohmic-Drude spectral density. We benchmark CCQME against numerically exact hierarchical equations of motion (HEOM) theory and compare to Redfield theory. Our results reveal that Redfield dynamics deviates increasingly from the HEOM reference as the system-bath coupling strength grows. In contrast, the recently proposed CCQME remains consistent with HEOM at intermediate coupling.
Published: 2026-03-23 11:48:46
Authors: María Romero-Colmenares, Katherine Vieira, Jeremy Tregloan-Reed, Yonggi Kim, Joh-Na Yoon, Ha-eun Kim, Hyo-ri Jeon, Chae-rin Kim, Christian Adam, Tobías C. Hinse, Mario Soto, Eduardo Unda-Sanzana, Penélope Longa-Peña, Ángel Otarola
Categories: astro-ph.IM, astro-ph.EP
Abstract:
Context. To better understand the observed brightness of low Earth orbit satellites, we must characterize their reflectivity, which in turn depends importantly on their bus designs. The reflectivity of a body can be described by Lambert's law, in terms of its albedo, cross-sectional area, range (distance), phase angles, and the mixing coefficient between diffuse and specular reflection components. Aims. We aim to analyze the reflectivity of more than 300 ONEWEB satellites using the diffuse Lambertian sphere, diffuse and specular Lambertian sphere, and the relative reflectance brightness models. Methods. Astrometric and photometric measurements, plus two-line elements (TLE) orbital information were used to compute the apparent and range-magnitude, as well as the relevant angles related to the orientation of the Sun, the satellites, and the observer. A differential evolution Monte Carlo algorithm was used to obtain each model's parameters that best fit the data. Results. All models can fit the mean observed brightness of the satellites but cannot describe the observed phase-angle-dependent brightness modulations. The residuals in all cases have a standard deviation of $\sim$0.6 magnitudes, while the observational photometric errors are on average $\sim$0.2 magnitudes. Conclusions. The studied brightness models, which depend on the relative Sun-body-observer position but are independent of the specific orientation of the reflecting body surface(s) with respect to the observer, cannot entirely explain the observed brightness of the ONEWEB constellation satellites. Accounting for the real shape and the changing attitude of the satellite, as well as the effect of Earth's albedo is needed to better explain satellite photometric observations
Published: 2026-03-23 11:34:06
Authors: Miquel Colom i Bernadich, Shi Dai, Federico Abbate, Matthew Kerr, Matteo Bachetti, Yash Bhargava, Sarah Buchner, Simon Johnston, Marta Burgay, Andrea Possenti, Rouhin Nag, Alessandro Ridolfi, Amodio Carleo, Alessandro Corongiu, Paulo C. C. Freire, Fernando Camilo, Weiwei Chen, Mario Cadelano, Dhanraj Risbud, Prajwal V. Padmanabh, David J. Champion, Michael Kramer, Benjamin Stappers, Maciej Serylak, Vishnu Balakrishnan, Matthew Bailes, Arunima Dutta, Laila Vleeschower Calas, Vivek Venkatraman Krishnan, Yunpeng Men
Categories: astro-ph.HE
Abstract:
Millisecond pulsars (MSPs) are powerful probes of globular clusters (GCs), tracing stellar evolution, cluster dynamics, and the local gravitational potential. We investigate the MSP population in GC Omega Centauri. We perform Fourier-domain acceleration and jerk searches on MeerKAT observations, and carry out pulsar timing using MeerKAT and Parkes Murriyang data spanning 2021-2025. We fold Fermi LAT and NICER photons using updated radio ephemerides to search for high-energy pulsations. We discover a new isolated MSP, PSR J1326-4728S (hereafter S), with a spin period of 4.538 ms and a dispersion measure of 96.24 cm$^3$pc. We update the orbital parameters of all known binary systems, with those of I, N, and Q differing significantly from previous estimates, and obtain new timing solutions for G, H, and K. Pulsars B, G, H, K, and L exhibit black widow-like properties, I, N and Q are found in wider binaries, with N and Q having >0.2 M$_\odot$ companions, and N showing a significant orbital eccentricity (e=0.093). Significant spin period derivatives are measured for eight pulsars and interpreted as arising from the cluster gravitational potential. No pulsed high-energy emission is detected from individual pulsars. The inferred line-of-sight accelerations are consistent with a King-model gravitational potential. While our measurements are insensitive to an intermediate-mass black hole with mass 10$^3$-10$^4$ M$_\odot$, they place an upper limit of <10$^5$ M$_\odot$ at 90% confidence. The high fraction of isolated MSPs and black widows systems, and possibly the eccentricity of N, are difficult to reconcile with MSP population predictions based solely on encounter rates. Instead, these properties likely reflect the complex evolutionary history of Omega Centauri, with part of its MSP population having formed in denser environments than the one observed today.
Published: 2026-03-23 11:28:27
Authors: V. B. Mendrot, A. S. de Castro, P. Alberto
Categories: quant-ph, math-ph
Abstract:
We study the Dirac equation in 3+1 dimensions with a general combination of scalar, vector and tensor interactions with arbitrary strengths, all of them described by central Coulomb potentials acting on a particular plane of motion. For the tensor coupling a constant term is also included, since this gives rise to an effective Coulomb potential, which is necessary for the formation of bound states in a pure tensor coupling configuration. The exact bound-state solutions for this generalized Coulomb problem are computed by exploiting the freedom in choosing the coefficients of the \textit{Ansätze} for the radial functions, which leads to wave functions in terms of generalized Laguerre polynomials. From the quantization condition, the exact energy spectrum is also determined and its dependence on the parameters of the potentials is discussed. We show that similar features of the equations for the problem in the plane and the spherically symmetric problem allow a simple and direct mapping between them, thereby providing the solution to the spherical Coulomb problem. Our results are validated by showing that the solutions correctly encompass several previous solutions available in the literature for particular cases of this problem, for which we further develop the analysis of the parameters. We also derive two new particular cases not yet reported in the literature: the case of breaking of spin and pseudospin symmetries by the addition of a Coulomb plus constant tensor potential and the problem of a scalar plus tensor Coulomb potentials.
Published: 2026-03-23 11:18:43
Authors: Alfredo González-Calvin, Juan F. Jiménez, Héctor García de Marina
Categories: cs.RO, math.DG
Abstract:
Path generation, the problem of producing smooth, executable paths from discrete planning outputs, such as waypoint sequences, is a fundamental step in the control of autonomous robots, industrial robots, and CNC machines, as path following and trajectory tracking controllers impose strict differentiability requirements on their reference inputs to guarantee stability and convergence, particularly for nonholonomic systems. Mollification has been recently proposed as a computationally efficient and analytically tractable tool for path generation, offering formal smoothness and curvature guarantees with advantages over spline interpolation and optimization-based methods. However, this mollification is subject to a fundamental geometric constraint: the smoothed path is confined within the convex hull of the original path, precluding exact waypoint interpolation, even when explicitly required by mission specifications or upstream planners. We introduce directional mollification, a novel operator that resolves this limitation while retaining the analytical tractability of classical mollification. The proposed operator generates infinitely differentiable paths that strictly interpolate prescribed waypoints, converge to the original non-differentiable input with arbitrary precision, and satisfy explicit curvature bounds given by a closed-form expression, addressing the core requirements of path generation for controlled autonomous systems. We further establish a parametric family of path generation operators that contains both classical and directional mollification as special cases, providing a unifying theoretical framework for the systematic generation of smooth, feasible paths from non-differentiable planning outputs.
Published: 2026-03-23 11:04:30
Authors: Bros Victor, Barbini Matilde, Gerard Patrick, Gatica-Perez Daniel
Categories: cs.CL, cs.CY
Abstract:
Interrogatives in news discourse have been examined in linguistics and conversation analysis, but mostly in broadcast interviews and relatively small, often English-language corpora, while large-scale computational studies of news rarely distinguish interrogatives from declaratives or differentiate their functions. This paper brings these strands together through a mixed-methods study of the "Politics of Questions" in contemporary French-language digital news. Using over one million articles published between January 2023 and June 2024, we automatically detect interrogative stances, approximate their functional types, and locate textual answers when present, linking these quantitative measures to a qualitatively annotated subcorpus grounded in semantic and pragmatic theories of questions. Interrogatives are sparse but systematically patterned: they mainly introduce or organize issues, with most remaining cases being information-seeking or echo-like, while explicitly leading or tag questions are rare. Although their density and mix vary across outlets and topics, our heuristic suggests that questions are overwhelmingly taken up within the same article and usually linked to a subsequent answer-like span, most often in the journalist's narrative voice and less often through quoted speech. Interrogative contexts are densely populated with named individuals, organizations, and places, whereas publics and broad social groups are mentioned much less frequently, suggesting that interrogative discourse tends to foreground already prominent actors and places and thus exhibits strong personalization. We show how interrogative stance, textual uptake, and voice can be operationalized at corpus scale, and argue that combining computational methods with pragmatic and sociological perspectives can help account for how questioning practices structure contemporary news discourse.
Published: 2026-03-23 11:04:19
Authors: V. E. Didenko, A. V. Korybut
Categories: hep-th
Abstract:
Higher-spin symmetry is known to mix lower-spin fields with higher-spin fields, creating a complex interaction picture where no closed finite field sector is expected to exist for dimensions greater than three. By studying the self-dual part of higher-spin interaction vertices in four dimensions, we show that gauge fields of spins greater than two can be consistently set to zero. In this case, the fields with helicities $-2\leqλ\leq 0$ form a closed sub-sector and also act as sources for positive helicities. For these lower spin fields, we identify their equations of motion. In particular, we show that self-dual gravity with a cosmological constant emerges as a unique rigid part of higher-spin interactions. Notably, its equations have a form that incorporates the Moyal star product, which is essential for generating the higher-spin algebra. Therefore, we demonstrate that self-dual gravity can be derived from higher-spin symmetries.
Published: 2026-03-23 11:03:10
Authors: Yanglin Deng, Tianyang Xu, Chunyang Cheng, Hui Li, Xiao-jun Wu, Josef Kittler
Categories: cs.CV
Abstract:
Infrared and visible image fusion(IVIF) combines complementary modalities while preserving natural textures and salient thermal signatures. Existing solutions predominantly rely on extensive sets of rigidly aligned image pairs for training. However, acquiring such data is often impractical due to the costly and labour-intensive alignment process. Besides, maintaining a rigid pairing setting during training restricts the volume of cross-modal relationships, thereby limiting generalisation performance. To this end, this work challenges the necessity of Strictly Paired Training Paradigm (SPTP) by systematically investigating UnPaired and Arbitrarily Paired Training Paradigms (UPTP and APTP) for high-performance IVIF. We establish a theoretical objective of APTP, reflecting the complementary nature between UPTP and SPTP. More importantly, we develop a practical framework capable of significantly enriching cross-modal relationships even with severely limited and unaligned training data. To validate our propositions, three end-to-end lightweight baselines, alongside a set of innovative loss functions, are designed to cover three classic frameworks (CNN, Transformer, GAN). Comprehensive experiments demonstrate that the proposed APTP and UPTP are feasible and capable of training models on a severely limited and content-inconsistent infrared and visible dataset, achieving performance comparable to that of a dataset 100$\times$ larger in SPTP. This finding fundamentally alleviates the cost and difficulty of data collection while enhancing model robustness from the data perspective, delivering a feasible solution for IVIF studies. The code is available at \href{https://github.com/yanglinDeng/IVIF_unpair}{\textcolor{blue}{https://github.com/yanglinDeng/IVIF\_unpair}}.
Published: 2026-03-23 11:01:41
Authors: Emre Akusta
Categories: econ.GN
Abstract:
This study analyses the potential of renewable energy to reduce inflationary pressures arising from energy imports in Turkiye. Annual data for the period 1980-2022 are used in the analysis. In this study, unit root properties are examined using the Zivot-Andrews and Lee-Strazicich tests, both of which explicitly account for structural breaks. Cointegration is investigated via the Johansen and Hatemi-J cointegration tests. Long-run coefficients are subsequently estimated using the DOLS and FMOLS estimators. The robustness of the empirical findings is further assessed using the ARDL approach. In addition, an interaction term is constructed to measure the impact of renewable energy in alleviating inflationary pressures arising from energy imports. The results show that energy imports and exchange rate have an increasing impact on inflation, while renewable energy and the interaction term have a decreasing impact. DOLS, FMOLS, and ARDL results support each other. Moreover, in both models, the impact of renewable energy in mitigating inflationary pressures stemming from energy imports is stronger than the direct disinflationary impact of renewable energy.
Published: 2026-03-23 10:58:31
Authors: Min Peng, Yuanjun Tang, Dianmeng Dong, Yang Zhang, Cheng Wang, Shulin Jiao, Xiaotong Ma, Shichao Zhang, Jingchen Wang, Huiying Wang, Yongxin Zhang, Huiping Zhu, Yue-Wen Fang, Fan Zhang, Zhenping Wu
Categories: cond-mat.mtrl-sci, cond-mat.mes-hall
Abstract:
The ultrawide-bandgap semiconductor $β$-Ga2O3 holds exceptional promise for next-generation power electronics and deep-ultraviolet optoelectronics, yet its widespread application is hindered by the lack of cost-effective, high-quality heteroepitaxial thin films. Here, we demonstrate an interpretable machine learning framework that efficiently navigates the complex, multiparameter process space of pulsed laser deposition (PLD) to achieve high-crystallinity $β$-Ga2O3 epitaxy on c-plane sapphire. By systematically benchmarking nine regression algorithms under limited experimental data conditions, we identify quadratic polynomial ridge regression as the optimal surrogate model, which combines predictive accuracy (R$^2$ $\approx$ 0.86) with full physical transparency through explicit analytical coefficients. Coupling this model with SHAP (SHapley Additive exPlanations) analysis and iterative experimental design, we construct a closed-loop optimization workflow that progressively refines the process-performance landscape over only three experimental rounds. This data-efficient strategy reduces the X-ray rocking curve (RC) full-width at half-maximum (FWHM) by 70$\%$ from > 3$^{\circ}$ to 0.92$^{\circ}$, which is the best reported value for PLD-grown $β$-Ga2O3 on sapphire. Intriguingly, concurrent modeling of surface roughness reveals that crystalline quality and surface morphology are governed by distinct dominant factors: temperature primarily controls bulk crystallinity, whereas oxygen pressure dictates surface kinetics. This decoupled mechanism, quantitatively captured for the first time via feature importance analysis, provides actionable physical insight for independent optimization of structural and morphological properties. Our work establishes a generalizable, resource-efficient paradigm for intelligent process development in oxide epitaxy and beyond.
Published: 2026-03-23 10:56:53
Authors: Richard Nies, Felix Parra
Categories: physics.plasm-ph
Abstract:
Theories of ion-scale microinstabilities in tokamaks and stellarators typically assume that the passing electrons respond adiabatically due to their fast propagation speed. However, when the magnetic shear becomes sufficiently small, ion-scale modes can extend far along the magnetic field and the non-adiabatic response of passing electrons becomes important. We derive a theory of extended modes at low magnetic shear through a multiscale expansion of the gyrokinetic equation. The theory elucidates the physics of the geodesic extended mode, a new type of microinstability. The new mode couples the non-adiabatic physics of both electrons and ions, unlike extended modes at magnetic shear of order unity. The theory is validated against gyrokinetic simulations and the parameter dependences of the new mode are studied.
Published: 2026-03-23 10:55:33
Authors: Tim Hageman
Categories: cs.CE
Abstract:
Standard phase-field fracture methods are rooted in brittle fracture theory and therefore do not inherently prescribe a material strength for crack nucleation, while also struggling to capture cohesive fracture behaviour. Recent eigenstrain-based formulations overcome both limitations by introducing fracture eigenstrains that decouple the strength surface from the fracture energy, but their implementation has so far relied on direct energy-minimization frameworks rather than standard finite-element procedures. In this work, we exploit the fact that the eigenstrains require no spatial gradients and reformulate the eigenstrain evolution as a local constitutive model, analogous to those used in plasticity, that is resolved at each integration point. As a result, the cohesive phase-field requires no additional global degrees of freedom beyond those of a standard phase-field formulation and can be readily integrated into existing finite-element codes. Two strength criteria are considered: a non-smooth criterion with independent tensile and shear strengths, and a smooth Drucker-Prager-like criterion that captures pressure-dependent strengthening under compression. Consistent tangent operators are derived for both criteria, ensuring robust convergence of the global Newton-Raphson solver. The framework is validated against three benchmark problems: a plate with a hole under tension and compression, a single-edge notched plate under shear, and a notched plate under dynamic loading. The results demonstrate mesh-independent and phase-field length-scale-independent behaviour, confirm that the fracture energy governs the transition between brittle and cohesive regimes, and show that complex phenomena such as crack branching under dynamic loading are naturally captured. All source codes are openly available.
Published: 2026-03-23 10:52:02
Authors: Lei Yang, Yi He, Fei Wu, Shilin Wang
Categories: cs.CV
Abstract:
Chinese mandarin visual speech recognition (VSR) is a task that has advanced in recent years, yet still lags behind the performance on non-tonal languages such as English. One primary challenge arises from the tonal nature of Mandarin, which limits the effectiveness of conventional sequence-to-sequence modeling approaches. To alleviate this issue, existing Chinese VSR systems commonly incorporate intermediate representations, most notably pinyin, within cascade architectures to enhance recognition accuracy. While beneficial, in these cascaded designs, the subsequent stage during inference depends on the output of the preceding stage, leading to error accumulation and increased inference latency. To address these limitations, we propose a cascade-free architecture based on multitask learning that jointly integrates multiple intermediate representations, including phoneme and viseme, to better exploit contextual information. The proposed semantic-guided local contrastive loss temporally aligns the features, enabling on-demand activation during inference, thereby providing a trade-off between inference efficiency and performance while mitigating error accumulation caused by projection and re-embedding. Experiments conducted on publicly available datasets demonstrate that our method achieves superior recognition performance.
Published: 2026-03-23 10:49:23
Authors: Lev Ayzenberg, Shady Abu-Hussein, Raja Giryes, Hayit Greenspan
Categories: cs.CV
Abstract:
Full data acquisition in MRI is inherently slow, which limits clinical throughput and increases patient discomfort. Compressed Sensing MRI (CS-MRI) seeks to accelerate acquisition by reconstructing images from under-sampled k-space data, requiring both an optimal sampling trajectory and a high-fidelity reconstruction model. In this work, we propose a novel active sampling framework that leverages the inherent discrete structure of a pretrained medical image tokenizer and a latent transformer. By representing anatomy through a dictionary of quantized visual tokens, the model provides a well-defined probability distribution over the latent space. We utilize this distribution to derive a principled uncertainty measure via token entropy, which guides the active sampling process. We introduce two strategies to exploit this latent uncertainty: (1) Latent Entropy Selection (LES), projecting patch-wise token entropy into the $k$-space domain to identify informative sampling lines, and (2) Gradient-based Entropy Optimization (GEO), which identifies regions of maximum uncertainty reduction via the $k$-space gradient of a total latent entropy loss. We evaluate our framework on the fastMRI singlecoil Knee and Brain datasets at $\times 8$ and $\times 16$ acceleration. Our results demonstrate that our active policies outperform state-of-the-art baselines in perceptual metrics, and feature-based distances. Our code is available at https://github.com/levayz/TRUST-MRI.
Published: 2026-03-23 10:42:35
Authors: Abhishek Kumar Maurya, Dinkar Mishra, Bhupesh Kumar, Ramesh C Sharma, Lal C Mangal, Binoy K Das, Brijesh Kumar
Categories: physics.plasm-ph, physics.comp-ph
Abstract:
In the present study, wakefield amplification via coherent resonant excitation using two co propagating laser pulses in a homogeneous plasma is investigated. The proposed scheme is based on linearly polarized leading seed pulse followed by a trailing pulse with identical or controlled parameters, enabling phase synchronized energy transfer to the plasma wave. By systematically varying the temporal pulse widths and inter pulse separation, conditions for resonant enhancement of the wakefield are established. Analytical modelling, supported by particle in cell simulations, reveals that maximum amplification occurs when the pulse separation approaches a quarter of the plasma wavelength, ensuring constructive interference of the plasma oscillations driven by successive pulses. Under optimal conditions, the coherent resonant excitation leads to a significant enhancement of the wakefield amplitude, reaching up to three times of that produced by a single laser pulse. The results demonstrate that precise control of pulse spacing and duration enables efficient energy coupling into plasma waves, providing a robust pathway for enhanced wakefield generation in laser plasma interaction regimes.
Published: 2026-03-23 10:24:58
Authors: Armand Rousselot, Joran Wendebourg, Ullrich Köthe
Categories: cs.LG
Abstract:
The performance of machine learning models is determined by the quality of their learned features. They should be invariant under irrelevant data variation but sensitive to task-relevant details. To visualize whether this is the case, we propose a method to analyze feature extractors by sampling from their fibers -- equivalence classes defined by their invariances -- given an arbitrary representative. Unlike existing work where a dedicated generative model is trained for each feature detector, our algorithm is training-free and exploits a pretrained diffusion or flow-matching model as a prior. The fiber loss -- which penalizes mismatch in features -- guides the denoising process toward the desired equivalence class, via non-linear diffusion trajectory matching. This replaces days of training for invariance learning with a single guided generation procedure at comparable fidelity. Experiments on popular datasets (ImageNet, CheXpert) and model types (ResNet, DINO, BiomedClip) demonstrate that our framework can reveal invariances ranging from very desirable to concerning behaviour. For instance, we show how Qwen-2B places patients with situs inversus (heart on the right side) in the same fiber as typical anatomy.
Published: 2026-03-23 10:18:13
Authors: Keyeun Lee, Sang Jung Kim
Categories: cs.SI, cs.CY
Abstract:
Cross-cutting commenting on social media is often imagined as a path to deliberation, yet exposure to opposing views frequently fuels hostility. To explain this dynamic, we introduce the concept of partisan warriors--commenters who cross ideological lines primarily to launch uncivil attacks against out-partisans. We analyze a large corpus of YouTube comments (N= 1,854,320) surrounding the 2024 U.S. second presidential debate. After filtering for toxicity and active participation, we use large language models to identify attack targets and operationalize partisan warrior behavior. Our analysis highlights four dynamics. First, cross-cutting commenters do not exhibit greater civility than those who remain within their ideological camps (RQ1). Second, audience reactions diverge by ideology: conservative audiences tended to reward hostile attacks on out-group leaders, whereas liberal audiences offered no comparable incentives and at times penalized such attacks (RQ2). Third, partisan warriors are notably more prevalent in conservative-leaning channels than in liberal ones; commenters restricted to conservative spaces were substantially more likely to engage in partisan warrior behavior compared to their liberal-only counterparts (RQ3). Finally, regarding environmental triggers, robustness checks suggest that this participation is an ecological phenomenon driven largely by channel-level heterogeneity rather than transient responses to individual video titles (RQ4). By shifting attention from the prevalence of incivility to its targets, rewards, and structural drivers, this study advances understanding of how partisan hostility is enacted and sustained in online spaces.
Published: 2026-03-23 10:11:40
Authors: Hanna Blazhko, Michał Wojtylak
Categories: math.NA
Abstract:
Rigorous, non-asymptotic bounds for the Puiseux expansion of the eigenvalue at infinity are given. Error analysis is provided. Further, the expected value of the eigenvector condition number of a randomly perturbed matrix is estimated. The latter result is applied to the Cayley transform of the linear pencil. Numerical simulations illustrating the theoretical findings are provided.
Published: 2026-03-23 10:05:51
Authors: Yuze Qin, Qingyong Li, Zhiqing Guo, Wen Wang, Yan Liu, Yangli-ao Geng
Categories: cs.LG, cs.AI
Abstract:
Precipitation nowcasting is critical for disaster mitigation and aviation safety. However, radar-only models frequently suffer from a lack of large-scale atmospheric context, leading to performance degradation at longer lead times. While integrating meteorological variables predicted by weather foundation models offers a potential remedy, existing architectures fail to reconcile the profound representational heterogeneities between radar imagery and meteorological data. To bridge this gap, we propose PW-FouCast, a novel frequency-domain fusion framework that leverages Pangu-Weather forecasts as spectral priors within a Fourier-based backbone. Our architecture introduces three key innovations: (i) Pangu-Weather-guided Frequency Modulation to align spectral magnitudes and phases with meteorological priors; (ii) Frequency Memory to correct phase discrepancies and preserve temporal evolution; and (iii) Inverted Frequency Attention to reconstruct high-frequency details typically lost in spectral filtering. Extensive experiments on the SEVIR and MeteoNet benchmarks demonstrate that PW-FouCast achieves state-of-the-art performance, effectively extending the reliable forecast horizon while maintaining structural fidelity. Our code is available at https://github.com/Onemissed/PW-FouCast.
Published: 2026-03-23 10:05:48
Authors: J. Wang, S. Jin, D. W. Xu, WeiKang Zheng, Thomas G. Brink, S. Komossa, 1 Alexei V. Filippenko, J. Y. Wei
Categories: astro-ph.GA
Abstract:
The evolutionary role of the so-called ``changing-look'' (CL) active galactic nucleus (AGN), which is characterized by spectral-type transitions within $\sim10$ yr, has been suggested in the past few years. By focusing on CL-AGNs having spectra similar to those of broad-line Seyfert 1 galaxies, some authors have proposed that CL-AGNs tend to be at a special evolutionary stage associated with intermediate-to-old stellar populations. Here we attempt to verify this evolutionary role by extending the sample to CL narrow-line Seyfert 1 (NLS1) galaxies, which are believed to be ``young'' AGNs with a less massive supermassive black hole and high accretion rate. Combining the recent large NLS1 catalog provided by Paliya et al. (2024) and the SDSS-V DR19 spectral survey returns only three CL-NLS1s out of a parent sample of 884 objects, reinforcing the rarity of CL-NLS1s. Subsequent spectral analysis shows that the evolutionary role mentioned above still holds, although CL-NLS1s tend to occupy the young end of the intermediate-old population. Finally, we propose that off-center SDSS spectra caused by the ``fiber drop'' effect have great potential for determining the properties of the narrow-line region of NLS1s.
Published: 2026-03-23 10:05:46
Authors: Tiberiu Harko, Shahab Shahidi
Categories: gr-qc, astro-ph.CO, hep-th
Abstract:
We investigate the influence of boundary terms in gravitational field theories, by considering that in the Einstein-Hilbert action the boundary can be described by a non-metric Weyl-type geometry. The gravitational action and the the field equations, are thus generalized to include new geometrical terms, coming from the non-metric nature of the boundary, and depending on the Weyl vector, and its covariant derivatives. The field equations obtained within this framework generalize the standard Einstein equations by including in their mathematical structure the Weyl vector, and its covariant derivatives. As an applications of the general formalism we investigate the cosmological evolution in a flat FLRW geometry. We obtain the generalized Friedmann equations, which contain extra terms depending on the Weyl vector and its derivatives, arising due to the presence of the Weylian boundary, and which describe an effective, time dependent dark energy. By imposing to the dark energy an equation of state parameter of the Barboza-Alcaniz type, the Friedmann equations can be solved numerically. We compare the predictions of the Weylian boundary gravitational theory with late-time observational data and the predictions of the $Λ$CDM paradigm. Our results show that the Weylian boundary cosmological models give a good description of the observational data, and they can reproduce almost exactly the predictions of the $Λ$CDM paradigm. Hence, the extension of gravitational theories through the addition of Weylian boundary terms, in which dark energy has a purely geometric origin, emerges as a viable alternative to standard general relativity.
Published: 2026-03-23 09:43:32
Authors: Matthew S. Clement, Nathan A. Kaib, Andre Izidoro, Rogerio Deienno
Categories: astro-ph.EP
Abstract:
It is thought that, sometime after their formation, the solar system's giant planets experienced a dynamical instability that caused their orbits to excite, diverge, and ejected one or more objects with masses comparable to the ice giants. A key feature of this model is that the planets experience encounters with other planetary bodies, and these encounters facilitate the capture of nearby small bodies as irregular satellites. Instability simulations indicate that planet-planet encounter distances can typically fall below 0.1 au, which is only roughly an order of magnitude larger than the radial extent of the modern planets' regular satellite systems. In this paper we model the effects of these encounters on the dynamical stability of the regular moons of Jupiter and Uranus. We tested encounter histories from 122 plausible outer solar system dynamical histories. We find that the survival probability for the Jovian and Uranian moon systems are both less than 15%. Moreover, we only identify one case where both Uranus and Jupiter's large satellites consistently survive the same instability. Interestingly, Jupiter's moons are most likely to survive in instabilities initialized with two smaller extra ice giants, and cases with one larger additional planet provide more favorable conditions for Uranian system survival. In either case, if Uranus encounters another ice giant at D<0.02 au, or one of the gas giants at D<0.1 au, satellite system destruction is effectively guaranteed. Wider encounters can also affect the system, particularly when they occur successively. Since the Laplace resonance likely would not be in place today if Jupiter's moons experienced an instability that led to collisions, our results indicate that Uranus' moons were likely perturbed to the point of collisions at least twice: as a result of both the impact that tilted the planet and the giant planet instability.
Published: 2026-03-23 09:38:39
Authors: Vivek Mishra, S. K. Agrawal
Categories: math.OC, math.DS
Abstract:
"Synchronization of two dynamical systems" is the term used to describe the phenomenon when two or more systems gradually change their states or behaviors to become similar or identical. This can happen in a lot of fields, such as physics, engineering, biology, and economics. Synchronization finds applications in neurology and communication systems. It is present in both man-made and organic systems. The nonlinear control synchronization technique for fractional-order time derivative systems is described in this article, where the Adams Basford Moulton method is used for solving the fractional-order system. The reliability and ease of applicability for two chaotic systems are demonstrated by the numerical simulation. Furthermore, in this article, both systems were kept in a chaotic condition while being synchronized with each other. The effects of synchronizing time and rearranging the derivatives are the most significant sections of this article.
Published: 2026-03-23 09:27:53
Authors: Martinez Emile, Garrido-Lucero Felipe, Grandi Umberto, Pérez-Salazar Sebastian
Categories: cs.GT
Abstract:
We introduce the \textit{prophet inequality with uncertain acceptance} model, in which a decision maker sequentially observes a sequence of independent options, each characterized by a value $x_i$ and an acceptance probability $p_i$, both sampled from a known joint distribution. At time $i$, the decision maker observes the value $x_i$ and must irrevocably and immediately decide whether to attempt to select it or to continue to the next time step. If the option is selected, the process terminates with probability $p_i$ and the decision maker obtains $x_i$; otherwise, she continues searching.
In this setting, two natural benchmarks arise: the \textit{value-aware decision-maker}, who knows all value realizations in advance but not the acceptance outcomes, and the \textit{full-knowledge prophet}, who knows all realizations beforehand and can choose the best option among those that will be accepted.
We characterize the worst-case competitive ratios between our defined agents and show that all these values equal $1/2$. In addition, we provide sufficient conditions under which the value-aware decision-maker surpasses the $1/2$-barrier against the more informed prophet. This demonstrates the (crucial) interest for the decision maker to improve her knowledge over the values rather than over the acceptances, and is obtained via a more general result that reduces the value-aware decision-maker's problem to a classical prophet inequality with scaled Bernoulli distributions, followed by a sequence of transformations that further reduce the problem to a unique optimization problem.