Published: 2026-04-10 16:19:40
Authors: Felipe Albino dos Santos
Categories: math.RT
Abstract:
We study Wakimoto-type free field constructions for superelliptic affine Lie algebras associated with coordinate rings $A=\mathbb{C}[t^{\pm1},u \mid u^m = p(t)]$, focusing on $\mathfrak{sl}_2$. We construct explicit operators on a tensor product of $m$ ghost Fock spaces, recovering the standard Wakimoto operator product expansions in the even sector and the correct $h^{(0)}$-charge relations in the odd sector. We then prove that the remaining mixed-sector brackets are obstructed within this class by two independent mechanisms: a charge-residue obstruction, arising from the K"{a}hler differential recurrence, and a Heisenberg branch-cut obstruction, caused by non-integer exponents in vertex operator products. These results yield a unified obstruction theorem for Wakimoto-type constructions in the superelliptic setting, explaining the failure of na"{i}ve free field realizations beyond the classical affine case.
Published: 2026-04-10 16:16:02
Authors: Shih-Yuin Lin, Bei-Lok Hu
Categories: hep-th, gr-qc, quant-ph
Abstract:
We compute the quantum radiation emitted by wavepackets of relativistic single electrons, both at rest and undergoing uniform acceleration in the Minkowski vacuum of the electromagnetic field. We find that the cubic terms in the original nonlinear action of electrodynamics should be considered in obtaining the quantum radiation to the leading order. We show that the quantum radiation from a single-electron wavepacket at rest vanishes exactly. For a uniformly accelerated electron, the quantum radiated power has secular growth in the long-time regime. We demonstrate that this secular growth has a classical interpretation, and argue that the resummed quantum radiation at late times would not diverge. Regarding experimental proposals for the detection of the Unruh effect from the quantum radiation in the `blind spots' of classical radiation we ascertain that quantum corrections in the two blind spots are fully contributed by the transverse deviation correlators, where the dominant contributions are irrelevant to the Unruh effect in electron microscopes.
Published: 2026-04-10 16:07:14
Authors: Lifeng Chen, Tianqi You, Hao Liu, Zhimin Bao, Jile Jiao, Xiao Han, Zhicai Ou, Tao Sun, Xiaofeng Mou, Xiaojie Jin, Yi Xu
Categories: cs.LG, cs.AI, eess.IV
Abstract:
Chest X-ray report generation (CXR-RG) has the potential to substantially alleviate radiologists' workload. However, conventional autoregressive vision--language models (VLMs) suffer from high inference latency due to sequential token decoding. Diffusion-based models offer a promising alternative through parallel generation, but they still require multiple denoising iterations. Compressing multi-step denoising to a single step could further reduce latency, but often degrades textual coherence due to the mean-field bias introduced by token-factorized denoisers. To address this challenge, we propose \textbf{ECHO}, an efficient diffusion-based VLM (dVLM) for chest X-ray report generation. ECHO enables stable one-step-per-block inference via a novel Direct Conditional Distillation (DCD) framework, which mitigates the mean-field limitation by constructing unfactorized supervision from on-policy diffusion trajectories to encode joint token dependencies. In addition, we introduce a Response-Asymmetric Diffusion (RAD) training strategy that further improves training efficiency while maintaining model effectiveness. Extensive experiments demonstrate that ECHO surpasses state-of-the-art autoregressive methods, improving RaTE and SemScore by \textbf{64.33\%} and \textbf{60.58\%} respectively, while achieving an \textbf{$8\times$} inference speedup without compromising clinical accuracy.
Published: 2026-04-10 16:04:25
Authors: Alberto Dinelli, Pietro Luigi Muzzeddu
Categories: cond-mat.stat-mech, cond-mat.soft
Abstract:
Understanding how microscopic motility shapes emergent collective behaviors is a challenging task in active matter, especially when self-propulsion is regulated by external cues or via quorum-sensing interactions. To address this problem, we derive a closed hydrodynamics for scalar active matter with spatially-regulated motility, under general hypotheses for the microscopic dynamics of the particles' orientations. We show that, at large scales, the contribution of the latter is entirely captured by the autocorrelation tensor of the orientations. This allows us to establish a macroscopic equivalence within a broad class of motility-regulated active systems, from single particles to active polymers. Our formalism allows us to reveal a new form of motility-induced phase separation for quorum-sensing active polymers, which we term anti-MIPS, where dense phases exhibit enhanced activity relative to dilute regions. Our theory shows that anti-MIPS generically arises for motility-regulated agents with internal structure, uncovering the existence of several distinct transition pathways.
Published: 2026-04-10 16:03:23
Authors: Mohammad Ali Vahedifar, Mojtaba Nazari, Qi Zhang
Categories: eess.SP, cs.LG
Abstract:
The Tactile Internet demands sub-millisecond latency and ultra-high reliability, as high latency or packet loss could lead to haptic control instability. To address this, we propose the Mode-Domain Architecture (MDA), a bilateral predictive neural network architecture designed to restore missing signals on both the human and robot sides. Unlike conventional models that extract features implicitly from raw data, MDA utilizes a novel Continuous-Orthogonal Mode Decomposition framework. By integrating an orthogonality constraint, we overcome the pervasive issue of "mode overlapping" found in state-of-the-art decomposition methods. Experimental results demonstrate that this structured feature extraction achieves high prediction accuracies of 98.6% (human) and 97.3% (robot). Furthermore, the model achieves ultra-low inference latency of 0.065 ms, significantly outperforming existing benchmarks and meeting the stringent real-time requirements of haptic teleoperation.
Published: 2026-04-10 16:01:25
Authors: Mohammad Omama, Gabriele Berton, Eric Foxlin, Yelin Kim
Categories: cs.CV
Abstract:
Precise and real-time visual localization is critical for applications like AR/VR and robotics, especially on resource-constrained edge devices such as smart glasses, where battery life and heat dissipation can be a primary concerns. While many efficient models exist, further reducing compute without sacrificing accuracy is essential for practical deployment. To address this, we propose asymmetric visual localization: a large Teacher model processes pre-mapped database images offline, while a lightweight Student model processes the query image online. This creates a challenge in matching features from two different models without resorting to heavy, learned matchers.
We introduce AsymLoc, a novel distillation framework that aligns a Student to its Teacher through a combination of a geometry-driven matching objective and a joint detector-descriptor distillation objective, enabling fast, parameter-less nearest-neighbor matching. Extensive experiments on HPatches, ScanNet, IMC2022, and Aachen show that AsymLoc achieves up to 95% of the teacher's localization accuracy using an order of magnitude smaller models, significantly outperforming existing baselines and establishing a new state-of-the-art efficiency-accuracy trade-off.
Published: 2026-04-10 16:01:13
Authors: Elena Eleftheriou, George Pallis, Marios Constantinides
Categories: cs.HC
Abstract:
Large Language Models (LLMs) are widely used by students, yet their tendency to provide fast and complete answers may discourage reflection and foster overconfidence. We examined how alternative LLM interaction designs support deeper thinking without excessively increasing cognitive burden. We conducted a two-phase mixed-methods study. In Phase 1, interviews with 16 Gen Z students informed the design of Deep3, a web-based system with three interaction modes: \emph{a)} future-self explanations, \emph{b)} contrastive learning, and \emph{c)} guided hints. In Phase 2, we evaluated Deep3 with 85 participants across two learning tasks. We found that a standard single-agent baseline produced high perceived understanding despite the lowest objective learning. In contrast, future-self explanations imposed higher cognitive workload yet yielded the closest alignment between perceived and actual understanding, while guided hints achieved the largest learning gains without a proportional increase in frustration. These findings show that effort, confidence, and learning systematically diverge in LLM-supported work.
Published: 2026-04-10 15:58:01
Authors: Shuntaro Tomizawa
Categories: math.DS
Abstract:
We study $C^r$ ($5 \le r \le \infty$) diffeomorphisms on closed manifolds of dimension at least three with a heteroclinic cycle between two hyperbolic periodic points. At each point, the unstable direction is one dimensional, and the stable and unstable eigenvalues closest to $1$ in modulus are real and simple. One heteroclinic connection is transverse and the other is non-transverse, and the product of those two eigenvalues is less than $1$ at one point and greater than $1$ at the other. Arbitrarily close to such a map, there are open sets in which a residual subset of diffeomorphisms has infinitely many attracting normally hyperbolic periodic circles. The proof uses a rescaling to the standard Hénon map and a corrected formula for the Lyapunov coefficient on its Neimark-Sacker (Andronov-Hopf) line.
Published: 2026-04-10 15:49:24
Authors: Itak Choi, Ilseung Park, Eni Halilaj, Inseung Kang
Categories: cs.RO
Abstract:
Designing generalizable control policies for lower-limb exoskeletons remains fundamentally constrained by exhaustive data collection or iterative optimization procedures, which limit accessibility to clinical populations. To address this challenge, we introduce a device-agnostic framework that combines physiologically plausible musculoskeletal simulation with reinforcement learning to enable scalable personalized exoskeleton assistance for both able-bodied and clinical populations. Our control policies not only generate physiologically plausible locomotion dynamics but also capture clinically observed compensatory strategies under targeted muscular deficits, providing a unified computational model of both healthy and pathological gait. Without task-specific tuning, the resulting exoskeleton control policies produce assistive torque profiles at the hip and ankle that align with state-of-the-art profiles validated in human experiments, while consistently reducing metabolic cost across walking speeds. For simulated impaired-gait models, the learned control policies yield asymmetric, deficit-specific exoskeleton assistance that improves both energetic efficiency and bilateral kinematic symmetry without explicit prescription of the target gait pattern. These results demonstrate that physiologically plausible musculoskeletal simulation via reinforcement learning can serve as a scalable foundation for personalized exoskeleton control across both able-bodied and clinical populations, eliminating the need for extensive physical trials.
Published: 2026-04-10 15:48:37
Authors: Dario Maio
Categories: cs.IR, cs.AI
Abstract:
Text embeddings are central to modern information retrieval and Retrieval-Augmented Generation (RAG). While dense models derived from Large Language Models (LLMs) dominate current practice, recent work has explored quantum-inspired alternatives motivated by the geometric properties of Hilbert-like spaces and their potential to encode richer semantic structure.
This paper presents an experimental framework for constructing quantum-inspired 1024-dimensional document embeddings based on overlapping windows and multi-scale aggregation. The pipeline combines semantic projections (e.g., EigAngle), circuit-inspired feature mappings, and optional teacher-student distillation, together with a fingerprinting mechanism for reproducibility and controlled evaluation.
We introduce a set of diagnostic tools for hybrid retrieval, including static and dynamic interpolation between BM25 and embedding-based scores, candidate union strategies, and a conceptual alpha-oracle that provides an upper bound for score-level fusion.
Experiments on controlled corpora of Italian and English documents across technical, narrative, and legal domains, using synthetic queries, show that BM25 remains a strong baseline, teacher embeddings provide stable semantic structure, and standalone quantum-inspired embeddings exhibit weak and unstable ranking signals. Distillation yields mixed effects, improving alignment in some cases but not consistently enhancing retrieval performance, while hybrid retrieval can recover competitive results when lexical and embedding-based signals are combined.
Overall, the results highlight structural limitations in the geometry of quantum-inspired embeddings, including distance compression and ranking instability, and clarify their role as auxiliary components rather than standalone retrieval representations.
Published: 2026-04-10 15:47:23
Authors: Wonbong Jang, Shikun Liu, Soubhik Sanyal, Juan Camilo Perez, Kam Woh Ng, Sanskar Agrawal, Juan-Manuel Perez-Rua, Yiannis Douratsos, Tao Xiang
Categories: cs.CV, cs.AI, cs.LG
Abstract:
Recovering camera parameters from images and rendering scenes from novel viewpoints have long been treated as separate tasks in computer vision and graphics. This separation breaks down when image coverage is sparse or poses are ambiguous, since each task needs what the other produces. We propose Rays as Pixels, a Video Diffusion Model (VDM) that learns a joint distribution over videos and camera trajectories. We represent each camera as dense ray pixels (raxels) and denoise them jointly with video frames through Decoupled Self-Cross Attention mechanism. A single trained model handles three tasks: predicting camera trajectories from video, jointly generating video and camera trajectory from input images, and generating video from input images along a target camera trajectory. Because the model can both predict trajectories from a video and generate views conditioned on its own predictions, we evaluate it through a closed-loop self-consistency test, demonstrating that its forward and inverse predictions agree. Notably, trajectory prediction requires far fewer denoising steps than video generation, even a few denoising steps suffice for self-consistency. We report results on pose estimation and camera-controlled video generation.
Published: 2026-04-10 15:37:24
Authors: François-Grégoire Bierwart, Alexandre Mauroy
Categories: math.DS
Abstract:
The Koopman operator is a powerful approach to global stability analysis of nonlinear systems, which provides a systematic procedure for Lyapunov function design. In this framework, Lyapunov functions are obtained through the eigenfunctions of the Koopman operator associated with the eigenvalues of the Jacobian matrix at the equilibrium. In practice, the eigenfunctions are approximated via a finite-dimensional representation of the operator, and there is no guarantee that the approximated spectrum accurately matches the true one. In this paper, we develop a kernel-based method to compute Koopman eigenfunctions and preserve the spectrum of the Jacobian matrix. This approach is suitable for stability analysis of high-dimensional systems thanks to the kernel trick. Moreover, the Lyapunov function candidate is validated through a scenario-based optimization technique that provides a reliable estimation of the region of attraction of the system.
Published: 2026-04-10 15:34:26
Authors: Owen Ekblad, Jeffrey Schenker
Categories: math-ph, math.DS, math.FA, quant-ph
Abstract:
We study the periodic properties of sequences of quantum channels sampled from an ergodic stochastic process satisfying a natural irreducibility condition. We relate these periodic properties to certain global spectral data defined by the sequence of quantum channels, proving a general Perron-Frobenius-type theorem. We give examples to motivate the theory and conclude with some open problems and conjectures.
Published: 2026-04-10 15:33:45
Authors: Junqi Liu, Yun Zhang, Xiaoxia Huang, Long Xu, Weisi Lin
Categories: eess.IV, cs.CV, cs.MM
Abstract:
Just Recognizable Difference (JRD) boosts coding efficiency for machine vision through visibility threshold modeling, but is currently limited to a single-task scenario. To address this issue, we propose a Multi-Task JRD (MT-JRD) dataset and an Attribute-assisted MT-JRD (AMT-JRD) model for Video Coding for Machines (VCM), enhancing both prediction accuracy and coding efficiency. First, we construct a dataset comprising 27,264 JRD annotations from machines, supporting three representative tasks including object detection, instance segmentation, and keypoint detection. Secondly, we propose the AMT-JRD prediction model, which integrates Generalized Feature Extraction Module (GFEM) and Specialized Feature Extraction Module (SFEM) to facilitate joint learning across multiple tasks. Thirdly, we innovatively incorporate object attribute information into object-wise JRD prediction through the Attribute Feature Fusion Module (AFFM), which introduces prior knowledge about object size and location. This design effectively compensates for the limitations of relying solely on image features and enhances the model's capacity to represent the perceptual mechanisms of machine vision. Finally, we apply the AMT-JRD model to VCM, where the accurately predicted JRDs are applied to reduce the coding bit rate while preserving accuracy across multiple machine vision tasks. Extensive experimental results demonstrate that AMT-JRD achieves precise and robust multi-task prediction with a mean absolute error of 3.781 and error variance of 5.332 across three tasks, outperforming the state-of-the-art single-task prediction model by 6.7% and 6.3%, respectively. Coding experiments further reveal that compared to the baseline VVC and JPEG, the AMT-JRD-based VCM improves an average of 3.861% and 7.886% Bjontegaard Delta-mean Average Precision (BD-mAP), respectively.
Published: 2026-04-10 15:27:49
Authors: Chao Jiang, Jingyu Huang, Miqing Li
Categories: cs.AI
Abstract:
Many-objective optimisation, a subset of multi-objective optimisation, involves optimisation problems with more than three objectives. As the number of objectives increases, the number of solutions needed to adequately represent the entire Pareto front typically grows substantially. This makes it challenging, if not infeasible, to design a search algorithm capable of effectively exploring the entire Pareto front. This difficulty is particularly acute in the Bayesian optimisation paradigm, where sample efficiency is critical and only a limited number of solutions (often a few hundred) are evaluated. Moreover, after the optimisation process, the decision-maker eventually selects just one solution for deployment, regardless of how many high-quality, diverse solutions are available. In light of this, we argue an idea that under a very limited evaluation budget, it may be more useful to focus on finding a single solution of the highest possible quality for the decision-maker, rather than aiming to approximate the entire Pareto front as existing many-/multi-objective Bayesian optimisation methods typically do. Bearing this idea in mind, this paper proposes a \underline{s}ingle \underline{p}oint-based \underline{m}ulti-\underline{o}bjective search framework (SPMO) that aims to improve the quality of solutions along a direction that leads to a good tradeoff between objectives. Within SPMO, we present a simple acquisition function, called expected single-point improvement (ESPI), working under both noiseless and noisy scenarios. We show that ESPI can be optimised effectively with gradient-based methods via the sample average approximation (SAA) approach and theoretically prove its convergence guarantees under the SAA. We also empirically demonstrate that the proposed SPMO is computationally tractable and outperforms state-of-the-arts on a wide range of benchmark and real-world problems.
Published: 2026-04-10 15:21:44
Authors: Mohamed Elfeki, Tu Trinh, Kelvin Luu, Guangze Luo, Nathan Hunt, Ernesto Montoya, Nandan Marwaha, Yannis He, Charles Wang, Fernando Crabedo, Alessa Castilo, Bing Liu
Categories: cs.AI
Abstract:
Frontier coding agents solve complex tasks when given complete context but collapse when specifications are incomplete or ambiguous. The bottleneck is not raw capability, but judgment: knowing when to act autonomously and when to ask for help. Current benchmarks are blind to this failure mode. They supply unambiguous detailed instructions and solely reward execution correctness, so an agent that makes a lucky guess for a missing requirement will score identically to one that would have asked to be certain.
We present HiL-Bench (Human-in-the-Loop Benchmark) to measure this selective escalation skill. Each task contains human-validated blockers (missing information, ambiguous requests, contradictory information) that surface only through progressive exploration, not upfront inspection. Our core metric, Ask-F1, the harmonic mean of question precision and blocker recall, captures the tension between over-asking and silent guessing; its structure architecturally prevents gaming through question spam.
Evaluation across SWE and text-to-SQL domains reveals a large universal judgment gap: no frontier model recovers more than a fraction of its full-information performance when deciding whether to ask. Failure analysis identifies three key help-seeking patterns: overconfident wrong beliefs with no gap detection; high uncertainty detection yet persistent errors; broad, imprecise escalation without self-correction. These consistent patterns confirm poor help-seeking is a model-level flaw, not task-specific. RL training on shaped Ask-F1 reward shows judgment is trainable: a 32B model improves both help-seeking quality and task pass rate, with gains that transfer across domains. The model does not learn domain-specific heuristics for when to ask; it learns to detect unresolvable uncertainty and act on it.
Published: 2026-04-10 15:16:28
Authors: Siddhartha Sahi, Hadi Salmasian, Vera Serganova
Categories: math.RT, math.RA
Abstract:
We classify irreducible finite-dimensional modules of a collection of real Lie superalgebras that includes the simple ones, their classical variants, complex Lie superalgebras after restriction of scalars, and all real Lie algebras. Our strategy is to reduce this classification to determining the orbits of the parity and conjugation functors on irreducible modules of the complexifications of the aforementioned algebras. Then we provide explicit results for the computation of these orbits. For Lie superalgebras of basic type or of type $\mathbf Q(n)$, our classification applies to any highest-weight parametrization of irreducible complex modules with respect to an arbitrary Borel subalgebra. As a consequence, in the special case of real simple Lie algebras we obtain a new perspective on the classification of real simple modules and establish a conceptual connection with Kostant's cascade of strongly orthogonal roots.
Published: 2026-04-10 14:37:54
Authors: Shipeng Zhu, Ang Chen, Na Nie, Pengfei Fang, Min-Ling Zhang, Hui Xue
Categories: cs.CV
Abstract:
Ancient inscriptions, as repositories of cultural memory, have suffered from centuries of environmental and human-induced degradation. Restoring their intertwined visual and textual integrity poses one of the most demanding challenges in digital heritage preservation. However, existing AI-based approaches often rely on rigid pipelines, struggling to generalize across such complex and heterogeneous real-world degradations. Inspired by the skill-coordinated workflow of human epigraphers, we propose EpiAgent, an agent-centric system that formulates inscription restoration as a hierarchical planning problem. Following an Observe-Conceive-Execute-Reevaluate paradigm, an LLM-based central planner orchestrates collaboration among multimodal analysis, historical experience, specialized restoration tools, and iterative self-refinement. This agent-centric coordination enables a flexible and adaptive restoration process beyond conventional single-pass methods. Across real-world degraded inscriptions, EpiAgent achieves superior restoration quality and stronger generalization compared to existing methods. Our work marks an important step toward expert-level agent-driven restoration of cultural heritage. The code is available at https://github.com/blackprotoss/EpiAgent.
Published: 2026-04-10 14:31:32
Authors: Peng Ding
Categories: cs.SE, cs.AI
Abstract:
The rapid proliferation of Large Language Model (LLM) providers--each exposing proprietary API formats--has created a fragmented ecosystem where applications become tightly coupled to individual vendors. Switching or bridging providers requires $O(N^2)$ bilateral adapters, impeding portability and multi-provider architectures. We observe that despite substantial syntactic divergence, the major LLM APIs share a common semantic core: the practical challenge is the combinatorial surface of syntactic variations, not deep semantic incompatibility. Based on this finding, we present LLM-Rosetta, an open-source translation framework built on a hub-and-spoke Intermediate Representation (IR) that captures the shared semantic core--messages, content parts, tool calls, reasoning traces, and generation controls--in a 9-type content model and 10-type stream event schema. A modular Ops-composition converter architecture enables each API standard to be added independently. LLM-Rosetta supports bidirectional conversion (provider-to-IR-to-provider) for both request and response payloads, including chunk-level streaming with stateful context management. We implement converters for four API standards (OpenAI Chat Completions, OpenAI Responses, Anthropic Messages, and Google GenAI), covering the vast majority of commercial providers. Empirical evaluation demonstrates lossless round-trip fidelity, correct streaming behavior, and sub-100 microsecond conversion overhead--competitive with LiteLLM's single-pass approach while providing bidirectionality and provider neutrality. LLM-Rosetta passes the Open Responses compliance suite and is deployed in production at Argonne National Laboratory. Code is available at https://github.com/Oaklight/llm-rosetta.
Published: 2026-04-10 14:29:55
Authors: Anish Ghoshal, Sk Jeesun, Kazunori Kohri
Categories: hep-ph, astro-ph.CO, hep-th
Abstract:
Measurement of $N_{\rm eff}$ in the CMB (Cosmic Microwave Background) observations, like Planck 2018 and BBN (Big Bang Nucleosynthesis) has already set stringent constraints on the interaction strength of light particles beyond the Standard Model (BSM). Despite such negligible couplings of such BSM particles to the visible sector, they are inevitably produced in the early universe through gravity-mediated processes. If a sizable density of light particles survives around CMB formation, they may act as dark radiation (DR) contributing to $N_{\rm eff}$ at CMB epoch. In this work, we study the production of such light BSM particles through the gravity-mediated scatterings in an effective field theory (EFT) setup assuming that all non-gravitational couplings of the BSM particle are negligible. Since the production is sensitive to the spin of the produced particle, we perform a concrete analysis for two representative cases: scalar dark Higgs DR and vector dark photons DR.Using the Planck 2018 observations, we find constraints on the reheating temperature ($T_{\rm RH}$) and background equation of state ($w_Φ$) during reheating in such scenarios featuring dark Higgs and dark photon. A comparative discussion involving gravity-mediated production of Dirac right-handed neutrinos ($ν_R$) and light axion-like particles (ALP) is also presented. Finally, for completeness, we also analyze the scenario where the production occurs through a generic spin-2 mediator characterized by an effective scale $Λ$ delineating the parameter space that is currently ruled out from Planck-2018 and can be probed by the future CMB experiments like LiteBird, Simon Observatory, CMB-S4, CMB-HD.
Published: 2026-04-10 14:29:38
Authors: Manuel Dias
Categories: math.SP, math.FA, math.PR
Abstract:
We study the spectral behavior as the sample size $n \to +\infty$ of integral operators defined by convolution of a non-negative symmetric kernel k with respect to empirical measures $μ_n = \frac{1}{n} \sum_{i=1}^n δ_{X_i}$, where $\{X_i\}_{i=1}^n$ are independent uniform samples from a compact probability metric space $(\mathcal{X},d,μ)$. Relaxing the usual positivity and continuity assumptions on k, we prove the convergence of these empirical operators to their continuous counterparts, and provide explicit convergence rates.
Published: 2026-04-10 14:24:36
Authors: Victoria G. G. Samboco, Ryan P. Keenan
Categories: astro-ph.GA
Abstract:
The relationship between the star formation rate surface density and the molecular gas surface density in galaxies is key to understanding galaxy evolution. To investigate the molecular Kennicutt-Schmidt (K-S) relation and its dependence on gas density, we analyze a uniform sample of 36 nearby galaxies from the AMISS survey, focusing on the CO(1-0), CO(2-1), and CO(3-2) transitions, which trace progressively denser and warmer molecular gas. Using statistical methods that combine binning with Markov Chain Monte Carlo (MCMC) fitting, we derive the slope, scatter, and intercept of the $Σ_{\mathrm{SFR}}$-$Σ_{\mathrm{CO}}$ relation for each transition. We find power-law slopes of 1.26, 1.14, and 1.07 for CO(1-0), CO(2-1), and CO(3-2), respectively, consistent with a trend toward increasingly linear star formation relations at higher-J transitions. This behavior supports the idea that denser gas is more directly linked to ongoing star formation and is consistent with previous findings of near-linear correlations between HCN or high-J CO luminosities and global SFR. The observed trend suggests an underlying relation between gas and SFR volume densities with a power-law index of $\sim$1.5, indicating enhanced star formation efficiency in denser environments. These findings underscore the critical role of dense gas in regulating star formation and highlight the importance of tracer selection and excitation conditions when interpreting the K-S relation across different environments.
Published: 2026-04-10 14:22:52
Authors: Silvia Gasparotto, Jann Zosso, Llibert Aresté Saló, Daniela D. Doneva, Stoytcho S. Yazadjiev
Categories: gr-qc
Abstract:
Gravitational-wave memory is a low-frequency, non-oscillatory component of the radiation field that provides a potentially powerful but as yet undetected probe of strong-field gravity. We present the first calculation of gravitational memory from full inspiral--merger--ringdown waveforms in a theory beyond general relativity, focusing on scalar-Gauss-Bonnet gravity as a theoretically well-motivated and numerically accessible extension of GR. Starting from the general memory formulas in Horndeski gravity, we derive explicit spin-weighted spherical-harmonic expressions for the tensor null memory in scalar-Gauss-Bonnet theory and evaluate them on existing numerical-relativity waveforms for both shift-symmetric and dynamically scalarizing binary black hole mergers. We find that the dominant effect is an indirect modification of the tensor memory through changes in the nonlinear merger dynamics, while the direct scalar contribution to the tensor memory remains suppressed by orders of magnitude for the systems considered in this work. For the largest deviations in our dataset, the final memory amplitude differs from the corresponding GR prediction by a few percent and by up to $\sim 4\%$ when compared to the GR template that minimizes the waveform mismatch in a detector-oriented analysis. We further show that including memory increases the mismatch between GR and scalar-Gauss-Bonnet waveforms by more than an order of magnitude, indicating that memory can provide complementary information for testing gravity with third-generation detectors, especially for low-mass binaries.
Published: 2026-04-10 14:19:30
Authors: Akash Srivastav, Sumit Srivastav, Vishnu P, Bhas Bapat
Categories: physics.atom-ph
Abstract:
We investigate ionic fragmentation of CO$_2^{n+}$~\mbox{($2\le n\le 4$)} produced in collisions with Ar$^{q+}$~\mbox{($4\le q\le 16$)} projectiles at a collision velocity of $\approx$~0.3~a.u. For most projectile and fragmentation channel combinations, the shape of the kinetic energy release distribution (KERD) differs with the electron capture mediated charge change (\mbox{$Δq$}) in the scattered projectile: KERD for \mbox{$Δq = 2$} is broader at high KER than for \mbox{$Δq =1$}. The difference generally diminishes with increasing projectile charge. Two deviations in this general trend are seen in the fragmentation of CO$_2^{3+}$, one for Ar$^{4+}$ impact in the high KER region and the other for Ar$^{6+}$ impact in the low KER region. The calculated reaction windows for multielectron capture within the framework of the extended classical over-the-barrier model (ECOBM) indicate that while ionization of the target occurs via multielectron capture, the scattered projectile may subsequently undergo multi-fold autoionization. Interpreting projectile autoionization to be a consequence of capture into highly excited states and high fragment KER to be a consequence of excitation of the ionized target to high-lying states, we find a strong dependence between the target and scattered projectile excitations.
Published: 2026-04-10 14:17:58
Authors: Maximilian Pierzyna, Sukanta Basu, Rudolf Saathof
Categories: physics.ao-ph
Abstract:
Accurate high-resolution vertical profiles of optical turbulence ($C_n^2$), which reflect local meteorology and topography, are crucial for ground-based optical astronomy and free-space optical communication. However, measuring these profiles or generating them with numerical weather models requires substantial operational or computational effort. In this work, we present OTProf, a deep-learning method that estimates high-resolution $C_n^2$ profiles from widely available coarse-resolution ERA5 reanalysis data. We evaluate the approach in the Netherlands and compare it with the commonly used Hufnagel-Valley model. Overall, OTProf reproduces the vertical structure of $C_n^2$ more accurately than Hufnagel-Valley and yields more accurate estimates of the Fried parameter $r_0$ and the scintillation index $σ_I^2$. As typical in machine learning, the $C_n^2$ predictions are slightly smoothed compared to reference data, especially in cases of rare strong turbulence. This smoothing affects the integrated parameters, sometimes leading to overly optimistic $r_0$ and $σ_I^2$ values. Despite this limitation, OTProf offers a more accurate, efficient, and physically consistent alternative to traditional analytical models and computationally expensive mesoscale models.
Published: 2026-04-10 14:06:23
Authors: Gokce Hacioglu, Serkan Vela
Categories: eess.SP
Abstract:
Multiple access techniques are vital for 5G and beyond. While Orthogonal Frequency Division Multiple Access (OFDMA) is standard, its high peak-to-average power ratio (PAPR) reduces energy efficiency in uplink transmissions. This paper presents Periodic OFDMA (P-OFDMA), a novel multiple access scheme with reduced PAPR and computational complexity. By assigning subcarriers in a periodic pattern across the entire frequency band, P-OFDMA enhances frequency diversity and simplifies allocation. We also introduce two precoded variants: P-OFDMA-DCT and P-OFDMA-DFT. Comprehensive simulations comparing P-OFDMA with OFDMA and SC-FDMA show that P-OFDMA-DFT consistently achieves the lowest PAPR. Furthermore, the standard P-OFDMA scheme outperforms SC-FDMA in PAPR for low subcarrier-per-user scenarios and achieves better bit error rate (BER) performance under high delay-spread conditions. Notably, P-OFDMA and its variants reduce transmitter-side processing by up to an eightfold factor compared to SC-FDMA, greatly benefiting low-complexity uplink devices. Although receiver complexity increases, the overall system processing load decreases, yielding improved energy efficiency. Thus, P-OFDMA offers a robust, energy-efficient uplink solution for future wireless networks.
Published: 2026-04-10 14:02:45
Authors: Marius-F. Danca
Categories: nlin.CD
Abstract:
This paper presents an improved Matlab routine, FO_LE, for the numerical computation of Lyapunov exponents of fractional-order systems modeled by Caputo's derivative. It is conceived as an enhanced version of the former FO_Lyapunov and FO_NC_Lyapunov codes for commensurate and non-commensurate orders, respectively. The proposed approach replaces the Gram-Schmidt orthogonalization procedure with QR-based reorthonormalization and uses the new quadratic LIL predictor-corrector scheme for the integration of the extended variational system. Compared with the former implementations, the present routine benefits from the higher order of the fractional integrator LIL and applies to both commensurate and non-commensurate models. Like the previous code, FO_LE retains the full memory structure of the underlying Caputo model. The Matlab code for the LIL solver and for the computation of Lyapunov exponents with FO_LE are provided, while a fast implementation of LIL for commensurate and non-commensurate orders, LIL_nc, is available on MathWorks File Exchange. A benchmark problem with exact solution is used to compare the LIL-based solver with ABM-type methods, whereas the Rabinovich-Fabrikant system illustrates the computation of Lyapunov exponents in different dynamical regimes. The results indicate that the proposed implementation is a compact, robust, and efficient tool for the numerical study of stability and chaos in fractional-order systems.
Published: 2026-04-10 13:52:18
Authors: Narges Rashvand, Shanle Yao, Armin Danesh Pazho, Babak Rahimi Ardabili, Hamed Tabkhi
Categories: cs.CV
Abstract:
Pose-based Video Anomaly Detection (VAD) has gained significant attention for its privacy-preserving nature and robustness to environmental variations. However, traditional frame-level evaluations treat video as a collection of isolated frames, fundamentally misaligned with how anomalies manifest and are acted upon in the real world. In operational surveillance systems, what matters is not the flagging of individual frames, but the reliable detection, localization, and reporting of a coherent anomalous event, a contiguous temporal episode with an identifiable onset and duration. Frame-level metrics are blind to this distinction, and as a result, they systematically overestimate model performance for any deployment that requires actionable, event-level alerts. In this work, we propose a shift toward an event-centric perspective in VAD. We first audit widely used VAD benchmarks, including SHT[19], CHAD[6], NWPUC[4], and HuVAD[25], to characterize their event structure. We then introduce two strategies for temporal event localization: a score-refinement pipeline with hierarchical Gaussian smoothing and adaptive binarization, and an end-to-end Dual-Branch Model that directly generates event-level detections. Finally, we establish the first event-based evaluation standard for VAD by adapting Temporal Action Localization metrics, including tIoU-based event matching and multi-threshold F1 evaluation. Our results quantify a substantial performance gap: while all SoTA models achieve frame-level AUC-ROC exceeding 52% on the NWPUC[4], their event-level localization precision falls below 10% even at a minimal tIoU=0.2, with an average event-level F1 of only 0.11 across all thresholds. The code base for this work is available at https://github.com/TeCSAR-UNCC/EventCentric-VAD.
Published: 2026-04-10 13:49:15
Authors: Yuze Su, Hongsong Wang, Jie Gui, Liang Wang
Categories: cs.CV
Abstract:
Reconstructing photorealistic and topology-aware human avatars from monocular videos remains a significant challenge in the fields of computer vision and graphics. While existing 3D human avatar modeling approaches can effectively capture body motion, they often fail to accurately model fine details such as hand movements and facial expressions. To address this, we propose Structure-aware Fine-grained Gaussian Splatting (SFGS), a novel method for reconstructing expressive and coherent full-body 3D human avatars from a monocular video sequence. The SFGS use both spatial-only triplane and time-aware hexplane to capture dynamic features across consecutive frames. A structure-aware gaussian module is designed to capture pose-dependent details in a spatially coherent manner and improve pose and texture expression. To better model hand deformations, we also propose a residual refinement module based on fine-grained hand reconstruction. Our method requires only a single-stage training and outperforms state-of-the-art baselines in both quantitative and qualitative evaluations, generating high-fidelity avatars with natural motion and fine details. The code is on Github: https://github.com/Su245811YZ/SFGS
Published: 2026-04-10 13:47:04
Authors: Xiaohan Wang, Chen Wu, Dawei Zhao, Guangwei Gao, Dianjie Lu, Guijuan Zhang, Linwei Fan, Xu Lu, Shuai Wu, Hang Wei, Zhuoran Zheng
Categories: eess.IV, cs.CV
Abstract:
Considering efficiency, ultra-high-definition (UHD) low-light image restoration is extremely challenging. Existing methods based on Transformer architectures or high-dimensional complex convolutional neural networks often suffer from the "memory wall" bottleneck, failing to achieve millisecond-level inference on edge devices. To address this issue, we propose a novel real-time UHD low-light enhancement network based on geometric feature fusion using Clifford algebra in 2D Euclidean space. First, we construct a four-layer feature pyramid with gradually increasing resolution, which decomposes input images into low-frequency and high-frequency structural components via a Gaussian blur kernel, and adopts a lightweight U-Net based on depthwise separable convolution for dual-branch feature extraction. Second, to resolve structural information loss and artifacts from traditional high-low frequency feature fusion, we introduce spatially aware Clifford algebra, which maps feature tensors to a multivector space (scalars, vectors, bivectors) and uses Clifford similarity to aggregate features while suppressing noise and preserving textures. In the reconstruction stage, the network outputs adaptive Gamma and Gain maps, which perform physically constrained non-linear brightness adjustment via Retinex theory. Integrated with FP16 mixed-precision computation and dynamic operator fusion, our method achieves millisecond-level inference for 4K/8K images on a single consumer-grade device, while outperforming state-of-the-art (SOTA) models on several restoration metrics.
Published: 2026-04-10 13:20:22
Authors: Ziyi Ding, Xiao-Ping Zhang
Categories: stat.ML, cs.LG, stat.CO
Abstract:
The Half-Trek Criterion (HTC) is the primary graphical tool for determining generic identifiability of causal effect coefficients in linear structural equation models (SEMs) with latent confounders. However, HTC is inherently node-wise: it simultaneously resolves all incoming edges of a node, leaving a gap of "inconclusive" causal effects (15-23% in moderate graphs). We introduce Iterative Identification Closure (IIC), a general framework that decouples causal identification into two phases: (1) a seed function S_0 that identifies an initial set of edges from any external source of information (instrumental variables, interventions, non-Gaussianity, prior knowledge, etc.); and (2) Reduced HTC propagation that iteratively substitutes known coefficients to reduce system dimension, enabling identification of edges that standard HTC cannot resolve. The core novelty is iterative identification propagation: newly identified edges feed back to unlock further identification -- a mechanism absent from all existing graphical criteria, which treat each edge (or node) in isolation. This propagation is non-trivial: coefficient substitution alters the covariance structure, and soundness requires proving that the modified Jacobian retains generic full rank -- a new theoretical result (Reduced HTC Theorem). We prove that IIC is sound, monotone, converges in O(|E|) iterations (empirically <=2), and strictly subsumes both HTC and ancestor decomposition. Exhaustive verification on all graphs with n<=5 (134,144 edges) confirms 100% precision (zero false positives); with combined seeds, IIC reduces the HTC gap by over 80%. The propagation gain is gamma~4x (2 seeds identifying ~3% of edges to 97.5% total identification), far exceeding gamma<=1.2x of prior methods that incorporate side information without iterative feedback.
Published: 2026-04-10 13:15:43
Authors: Marco Aiello, Mina Alipour, Antonio Brogi, Rafael Capilla, Lidia Fuentes, Ilche Georgievski, Gabriele Gianini, Mahyar Tourchi Moghaddam, Monica Vitali, Sebastian Werner
Categories: cs.SE, cs.CY
Abstract:
The sustainability impacts of ICT systems are difficult to assess and govern due to structural complexity, fragmented measurement practices, and unclear responsibilities across system layers. We argue that these challenges cannot be addressed solely by metrics and motivate the need for a shared Green ICT reference framework that integrates sustainability across multiple perspectives and domains, lifecycle phases, and governance contexts. We present an initial framework developed within the Informatics Europe Green ICT Working Group as a first step towards a comprehensive reference framework.
Published: 2026-04-10 13:13:56
Authors: Vipooshan Vipulananthan, Charith D. Chitraranjan
Categories: cs.CV
Abstract:
Traffic accidents are a leading cause of fatalities and injuries across the globe. Therefore, the ability to anticipate hazardous situations in advance is essential. Automated accident anticipation enables timely intervention through driver alerts and collision avoidance maneuvers, forming a key component of advanced driver assistance systems. In autonomous driving, such predictive capabilities support proactive safety behaviors, such as initiating defensive driving and human takeover when required. Using dashcam video as input offers a cost-effective solution, but it is challenging due to the complexity of real-world driving scenes. Accident anticipation systems need to operate in real-time. However, current methods involve extracting features from each detected object, which is computationally intensive. We propose VAGNet, a deep neural network that learns to predict accidents from dash-cam video using global features of traffic scenes without requiring explicit object-level features. The network consists of transformer and graph modules, and we use the vision foundation model VideoMAE-V2 for global feature extraction. Experiments on four benchmark datasets (DAD, DoTA, DADA, and Nexar) show that our method anticipates accidents with higher average precision and mean time-to-accident while being computationally more efficient compared to existing methods.
Published: 2026-04-10 12:50:57
Authors: Gautier Hénique, William Le, Gabriel Dayan, Coralie Brodeur, Kristoff Nelson, Apostolos Christopoulos, Edith Filion, Phuc-Felix Nguyen-Tan, Laurent Letourneau-Guillon, Houda Bahig, Samuel Kadoury
Categories: eess.IV, cs.CV
Abstract:
Extranodal extension (ENE) is an emerging prognostic factor in human papillomavirus (HPV)-associated oropharyngeal cancer (OPC), although it is currently omitted as a clinical staging criteria. Recent works have advocated for the inclusion of iENE as a prognostic marker in HPV-positive OPC staging. However, several practical limitations continue to hinder its clinical integration, including inconsistencies in segmentation, low contrast in the periphery of metastatic lymph nodes on CT imaging, and laborious manual annotations. To address these limitations, we propose a fully automated end-to-end pipeline that uses computed tomography (CT) images with clinical data to assess the status of nodal ENE and predict treatment outcomes. Our approach includes a hierarchical 3D semi-supervised segmentation model designed to detect and delineate relevant iENE from radiotherapy planning CT scans. From these segmentations, a set of radiomics and deep features are extracted to train an imaging-detected ENE grading classifier. The predicted ENE status is then evaluated for its prognostic value and compared with existing staging criteria. Furthermore, we integrate these nodal features with primary tumor characteristics in a multimodal, attention-based outcome prediction model, providing a dynamic framework for outcome prediction. Our method is validated in an internal cohort of 397 HPV-positive OPC patients treated with radiation therapy or chemoradiotherapy between 2009 and 2020. For outcome prediction at the 2-year mark, our pipeline surpassed baseline models with 88.2% (4.8) in AUC for metastatic recurrence, 79.2% (7.4) for overall survival, and 78.1% (8.6) for disease-free survival. We also obtain a concordance index of 83.3% (6.5) for metastatic recurrence, 71.3% (8.9) for overall survival, and 70.0% (8.1) for disease-free survival, making it feasible for clinical decision making.
Published: 2026-04-10 12:49:16
Authors: Hongxing Chen, Jiangsheng Hu, Xiaoyan Yang
Categories: math.RA, math.AC
Abstract:
Quasi-projective dimension of modules over associative rings is generalized in this paper to the one of complexes of modules. Basic properties of this dimension are established, including a comparison result with projective dimension and a derived Auslander-Buchsbaum formula for complexes of finite quasi-projective dimension. Several sufficient conditions are provided for a commutative noetherian local ring to be a complete intersection under the assumption that each finitely generated module has finite quasi-projective dimension. This provides some positive answers to an open question on quasi-projective dimension proposed by Gheibi-Jorgensen-Takahashi. Moreover, the behavior of quasi-projective dimension under taking the quotient of a commutative ring modulo a regular sequence is investigated, and some partial results toward the change-of-rings question on quasi-projective dimension are given.
Published: 2026-04-10 12:38:00
Authors: Marissa Vlasblom, Aditya M. Arabhavi, Niels de Klerk, Inga Kamp, Benoît Tabone, Ewine F. van Dishoeck
Categories: astro-ph.EP
Abstract:
Nitrogen is a key element for building habitable worlds, yet only a small fraction of the available N-budget of planet-forming disks has been detected. In particular, the lack of any IR NH$_3$ detection is striking, as this molecule is predicted to be rather abundant in the warm, inner regions of protoplanetary disks, and therefore potentially readily incorporated into (giant) planets' atmospheres. We present a combined modeling and observational study of N-bearing molecules in planet-forming disks, using detailed thermo-chemical disk models that investigate the sensitivity of N-containing molecules to the bulk elemental composition of the disk. Our models predict a strong increase in HCN flux with high C/H, and conversely a strong increase in flux from NO when O/H is high. The flux from NH$_3$ is not very sensitive to O/H, but does decrease at high C/H due to competition with HCN. However, the absolute NH$_3$ flux predicted by our model is not large enough to be detected with JWST-MIRI, even when N/H is enhanced by an order of magnitude. The flux from NO, on the other hand, is potentially detectable, and could therefore provide further insights into the N-budget of the inner disk. Using a cross-correlation technique, we search for NH$_3$ and NO detections in three disks, GW Lup, Sz 98, and V1094 Sco. We do not find any NH$_3$ detections, and only one tentative NO detection in V1094 Sco, though this needs further study to be confirmed. Additionally, we demonstrate that future facilities in the FIR may provide a better opportunity to detect NH$_3$ and thereby draw a comparison to the NH$_3$ budget known to be present in interstellar ices.
Published: 2026-04-10 12:35:14
Authors: Sheng Jin, Dong-Hong Wu, Xiao-Ling Xu, Jianghui Ji
Categories: astro-ph.EP, astro-ph.IM
Abstract:
Transit timing variations (TTV) are considered a tool for constraining the masses of transiting planets in the absence of radial-velocity data. Although theoretical studies have long revealed that TTV mass determinations intrinsically suffer from degeneracies, existing analyses of TTV data typically report a single-mode solution under a model with a specified number of planets. This is because fitting TTV curves in the high-dimensional solution space of TTV posterior is extremely challenging; even locating a single solution requires substantial computational resources. We developed an efficient mode-first searching algorithm that can locate multiple solutions in a single MCMC run. We applied this algorithm to Kepler-9 b and c, which have the highest-quality TTV data. We found that the observed TTV can be reproduced by many combinations of planetary masses spanning a broad range, rather than the previously assumed precise determination. The mass of Kepler-9 b can range from 31.6 to 47.1 $M_{\oplus}$, while that of Kepler-9 c can range from 21.8 to 32.3 $M_{\oplus}$, and even more broadly under looser constraints. These degenerate solutions follow a linear relationship under a tight mass ratio between the two planets, consistent with previous theoretical predictions. Furthermore, we demonstrate that achieving a globally converged posterior distribution for Kepler-9's TTV is impossible using a sampling algorithm that preserves the Markovian property. This underscores the need for caution when interpreting results from sampling algorithms that lack mathematical guarantees of global convergence.
Published: 2026-04-10 12:28:32
Authors: Jiawen Deng, Wei Li, Wentao Zhang, Ziyun Jiao, Fuji Ren
Categories: cs.CL
Abstract:
Intelligent dialogue systems are increasingly deployed in emotionally and ethically sensitive settings, where failures in either emotional attunement or ethical judgment can cause significant harm. Existing dialogue models typically address empathy and ethical safety in isolation, and often fail to adapt their behavior as ethical risk and user emotion evolve across multi-turn interactions. We formulate ethical-emotional alignment in dialogue as an explicit turn-level decision problem, and propose \textsc{EthicMind}, a risk-aware framework that implements this formulation in multi-turn dialogue at inference time. At each turn, \textsc{EthicMind} jointly analyzes ethical risk signals and user emotion, plans a high-level response strategy, and generates context-sensitive replies that balance ethical guidance with emotional engagement, without requiring additional model training. To evaluate alignment behavior under ethically complex interactions, we introduce a risk-stratified, multi-turn evaluation protocol with a context-aware user simulation procedure. Experimental results show that \textsc{EthicMind} achieves more consistent ethical guidance and emotional engagement than competitive baselines, particularly in high-risk and morally ambiguous scenarios.
Published: 2026-04-10 12:24:39
Authors: Philip Semrén, Michael Bradley, João M. S. Oliveira, M. Piedade Machado Ramos
Categories: gr-qc
Abstract:
A simplified relativistic kinetic theory for gases with internal degrees of freedom, based on a BGK-type collision term, is considered. First the Boltzmann equation is rewritten in tetrad form and then thermal coefficients are determined to first order in the Chapman-Enskog expansion for general spacetimes. The results are used to construct a self-consistent system of first order differential equations, equivalent to the Einstein-Boltzmann system, for some spatially homogeneous models with viscosity and heat flow.
Published: 2026-04-10 12:09:06
Authors: Yuqin Lan, Gen Li, Yuanze Hu, Weihao Shen, Zhaoxin Fan, Faguo Wu, Xiao Zhang, Laurence T. Yang, Zhiming Zheng
Categories: cs.CV, cs.AI
Abstract:
Vision-Language Models (VLMs) are powerful but remain vulnerable to multimodal jailbreak attacks. Existing attacks mainly rely on either explicit visual prompt attacks or gradient-based adversarial optimization. While the former is easier to detect, the latter produces subtle perturbations that are less perceptible, but is usually optimized and evaluated under homogeneous open-source surrogate-target settings, leaving its effectiveness on commercial closed-source VLMs under heterogeneous settings unclear. To examine this issue, we study different surrogate-target settings and observe a consistent gap between homogeneous and heterogeneous settings, a phenomenon we term surrogate dependency. Motivated by this finding, we propose Mosaic, a Multi-view ensemble optimization framework for multimodal jailbreak against closed-source VLMs, which alleviates surrogate dependency under heterogeneous surrogate-target settings by reducing over-reliance on any single surrogate model and visual view. Specifically, Mosaic incorporates three core components: a Text-Side Transformation module, which perturbs refusal-sensitive lexical patterns; a Multi-View Image Optimization module, which updates perturbations under diverse cropped views to avoid overfitting to a single visual view; and a Surrogate Ensemble Guidance module, which aggregates optimization signals from multiple surrogate VLMs to reduce surrogate-specific bias. Extensive experiments on safety benchmarks demonstrate that Mosaic achieves state-of-the-art Attack Success Rate and Average Toxicity against commercial closed-source VLMs.
Published: 2026-04-10 12:06:21
Authors: Dr James W. Trayford
Categories: astro-ph.IM, physics.ed-ph, physics.soc-ph
Abstract:
The 2025 UK National Astronomy Meeting (NAM) in Durham played host to a session titled "Unseen Astronomy", involving a variety of astronomy researchers in diverse fields. This unique meeting focussed on a number of novel projects exploring alternatives to purely visual means of display in Astronomy, encompassing spheres of education, communication and research, and straddling both accessible and general use applications. The successful inclusion of such a session at a major conference reflects the explosion of interest in multimodal astronomy in recent years, and hints at its transformative potential. Here, I aim to outline and motivate the topic of multi-modal science and consider its exciting potential. I will discuss this in the context of our own work in the area, the community building being undertaken to bring together researchers considering multi-modality, and efforts to impact astronomy at large.
Published: 2026-04-10 11:55:13
Authors: Wong Kam-Kwai, Yi-Lin Ye, Wai Tong, Haobo Li, Kentaro Takahira, Aastha Bhatta, Sunil Poudyal, Charles Wang Wai Ng, Huamin Qu, Leni Yang
Categories: cs.HC
Abstract:
Landslides pose a significant threat to public safety, but their dynamic processes are difficult to analyze from post-event observation alone. Computational simulation is therefore essential, but it generates vast, abstract datasets that create a cognitive gap between the analyst and the real-world, physical terrain. While Immersive Analytics (IA) begins to bridge this gap by visualizing data in 3D, we explore how these systems evolve beyond abstract data and integrate data visceralization to enhance Situational Awareness (SA). We present LandSAR, an immersive analytics system that enhances SA for landslide analysis by visceralizing landslide data through integrated simulations and visualizations. LandSAR supports real-time simulations of landslide dynamics, prevention strategies, and climate impacts, enabling multi-perspective what-if analyses. The system uses 3D-printed terrain models as tangible interfaces to facilitate haptic feedback and enable gesture-based exploration, allowing for intuitive geographical perception. Expert interviews and workshops demonstrate that LandSAR effectively improves SA and engagement.
Published: 2026-04-10 11:54:08
Authors: Ravshan Ashurov, Damir Shamuratov
Categories: math.AP
Abstract:
This paper is devoted to the investigation of the backward problem for a multi-term time-fractional diffusion equation. Backward problems for fractional diffusion equations are typically studied using regularization methods due to their ill-posedness in the sense of Hadamard; that is, a small change in u(T) may lead to large changes in the initial data. Nevertheless, we show that if sufficiently smooth current data are considered, then the solution exists, is unique, and is stable. A principal difficulty in the analysis of the backward problem stems from the structure of the solution, in which the multinomial Mittag-Leffler function appears in the denominator. Accordingly, a precise characterization of the asymptotic behavior of this function is required. Such asymptotic properties are nontrivial and have been rigorously established in the authors' recent work, which serves as a fundamental basis for the present study. In addition, we investigate the conditional stability of the backward problem. It is shown that, although the problem is ill-posed in general, stability can be restored under an appropriate a priori bound imposed on the initial data. The main novelty of the paper lies in proving the best smoothing property of the solution, showing that it belongs to the domain of the operator A for any positive time.
Published: 2026-04-10 11:46:03
Authors: Christopher Seibel, Tobias Held, Markus Uehlein, Baerbel Rethfeld
Categories: cond-mat.mtrl-sci
Abstract:
Photoexcited hot carriers in solids can drive processes, such as photocatalytic reactions on the surface, beyond those available in thermal equilibrium. Hot-electron-mediated reaction pathways are limited by the thermalization of the nonequilibrium electron distribution through microscopic scattering events. Commonly, thermalization is exclusively attributed to electron-electron scattering, whereas electron-phonon scattering is considered relevant mainly for the energy equilibration with the lattice. With a kinetic model based on full Boltzmann collision integrals, we demonstrate that each scattering mechanism alone can thermalize the electron distribution, albeit along different trajectories in phase space. We find an opposite dependence on the excitation strength of the respective thermalization times and show that both processes can become comparable for weak excitations, corresponding to a sample temperature increase of a few Kelvin. Our results unravel the contributions of electron-electron and electron-phonon scattering to the thermalization across the full range of experimental excitation strengths up to the melting regime, thus facilitating the prediction of thermalization times for hot-carrier-based applications.
Published: 2026-04-10 11:34:20
Authors: Nordin Breugelmans, Felix Heyen, Jorgen D'Hondt, Michael Tytgat, Gerrit Van Onsem
Categories: hep-ph
Abstract:
As the search for observable deviations from the Standard Model of particle physics remains to be of significant interest, effective field theory (EFT) continues to be a popular method to parametrize such effects. In this work, a first-time investigation is performed of the unique capability of measurements of charm quark associated Higgs boson production (cH) in proton-proton collisions at the CERN Large Hadron Collider to constrain a set of dimension-six EFT operators. The phenomenology of these operators is discussed and a proposed analysis strategy is presented, with a focus on $\mathrm{H}\rightarrow \mathrm{Z}\mathrm{Z}^{*}\rightarrow 4μ$ decays, using a generic detector simulation that is parametrized to reflect the response of the CMS detector at the LHC. From this, expected 95% CL upper limits are derived for the Wilson coefficients of individual operators by considering yield and shape effects in the spectra of the four-muon invariant mass $m_{4μ}$ and leading jet transverse momentum $p_{T}$. Scenarios with simultaneous contributions from two operators are also considered. Finally, potential analysis improvements that may be implemented in an experimental context are outlined.
Published: 2026-04-10 11:11:32
Authors: Kai Partmann, Christian Wieners, Michael Ortiz, Kerstin Weinberg
Categories: cs.CE
Abstract:
Peridynamics formulates the balance of linear momentum as an integro-differential equation, making it naturally suited for fracture modeling without special treatment of discontinuities. The bond-associated correspondence formulation provides a highly accurate peridynamic framework by computing bond-wise deformation gradients that are free of zero-energy modes and yield accurate results even near boundaries. However, the traditional fracture approach based on irreversible bond deletion can compromise this formulation, as the progressive removal of bonds degrades the nonlocal approximation of the deformation gradient and can lead to numerical instabilities. In this work, a novel phase-field peridynamics approach is introduced that avoids these instabilities. Instead of deleting bonds, the energetic contribution of each bond is continuously degraded through a bond phase-field parameter, while a separate kinematic degradation function preserves the accuracy of the nonlocal deformation gradient approximation. The normalization constant ensuring thermodynamic consistency with Griffith's fracture theory is derived analytically for general spherical kernel functions as a ratio of two one-dimensional integrals. Numerical examples including mode I and mode II fracture, the boundary tension test with different kernel functions and horizon ratios, and the Kalthoff-Winkler experiment demonstrate the stability, accuracy, and consistency of the proposed approach.
Published: 2026-04-10 11:08:04
Authors: Mohamadreza Delbari, Robin Neuder, Alejandro Jiménez-Sáez, Qikai Zhou, Vahid Jamali
Categories: eess.SP
Abstract:
Liquid crystal (LC) is a promising hardware solution for implementing large RISs, as it is cost-effective, energy efficient, scalable, and capable of providing continuous phase shifts with low power consumption. However, the phase shift response of LC-based RISs is inherently frequency dependent. If unaddressed, this characteristic leads to performance degradation, particularly in wideband scenarios. This issue is especially critical in secure communication applications, where minor phase shift variations across elements can result in considerable information leakage. This paper addresses these frequency-induced variations by developing a physics-based model for an LC unit cell across varying frequencies and proposing a novel phase shift design framework that maximizes secure communication across all subcarriers. Given the large number of elements in millimeter wave (mmWave) LC-RISs, acquiring full channel state information (CSI) is often impractical. Therefore, we optimize the phase shifts based solely on the locations of the legitimate mobile users (MUs) and potential eavesdroppers. Rather than targeting a single user point, the RIS is designed to illuminate a broader area. This approach enhances communication reliability for the MUs and mitigates performance degradation caused by location estimation errors. To solve the problem, we introduce both a semi-definite programming (SDP)-based solution and a low complexity heuristic method. While the SDP-based approach yields superior performance, it incurs higher computational complexity. Conversely, the scalable method exhibits a much slower scaling of complexity, which makes it highly suitable for extremely large RISs. Simulation results demonstrate that both algorithms improve the secrecy rate compared to baseline methods. Finally, the proposed design is validated through experimental evaluations on an LC RIS setup.
Published: 2026-04-10 10:51:06
Authors: Francesco Rosini, Simone Pacetti
Categories: hep-ph, math-ph
Abstract:
We present and demonstrate a version of Levinson's theorem especially dedicated to the asymptotic behavior of form factor phases. Indeed, as required by analyticity, form factors are multi-valued complex functions of a square four-momentum defined in the complex plane with a cut along the positive real axis. Their phases evaluated on the upper edge of this cut, i.e., on the time-like region, tend asymptotically to integer multiples of $π$ radians. The Levinson's theorem establishes a univocal relation between such multiples and properties of form factors related to the dynamics of the electromagnetic interaction of the corresponding hadrons.
Published: 2026-04-10 10:34:49
Authors: Kobi Hackenburg, Luke Hewitt, Caroline Wagner, Ben M. Tappin, Christopher Summerfield
Categories: cs.CY, cs.AI, cs.HC
Abstract:
There is substantial concern about the ability of advanced artificial intelligence to influence people's behaviour. A rapidly growing body of research has found that AI can produce large persuasive effects on people's attitudes, but whether AI can persuade people to take consequential real-world actions has remained unclear. In two large preregistered experiments N=17,950 responses from 14,779 people), we used conversational AI models to persuade participants on a range of attitudinal and behavioural outcomes, including signing real petitions and donating money to charity. We found sizable AI persuasion effects on these behavioural outcomes (e.g. +19.7 percentage points on petition signing). However, we observed no evidence of a correlation between AI persuasion effects on attitudes and behaviour. Moreover, we replicated prior findings that information provision drove effects on attitudes, but found no such evidence for our behavioural outcomes. In a test of eight behavioural persuasion strategies, all outperformed the most effective attitudinal persuasion strategy, but differences among the eight were small. Taken together, these results suggest that previous findings relying on attitudinal outcomes may generalize poorly to behaviour, and therefore risk substantially mischaracterizing the real-world behavioural impact of AI persuasion.
Published: 2026-04-10 10:06:06
Authors: G. Medoš, L. Vidmar
Categories: cond-mat.stat-mech, cond-mat.quant-gas, cond-mat.str-el, quant-ph
Abstract:
While the eigenstate entanglement entropy has been extensively studied for fermionic systems, much less is known about bosonic systems. Here, we study the entanglement entropy of mid-spectrum eigenstates of Bose-Hubbard models, focusing on weakly disordered models with and without particle-number conservation, and contrasting them with the translationally-invariant model. We analyze the volume-law and O(1) contributions to the entanglement entropy via the averages over mid-spectrum eigenstates and the corresponding distributions. We derive the volume-law coefficient of the entanglement entropy by generalizing the mean-field approach from [Phys. Rev. Lett. 119, 220603 (2017)] to many-body systems with a tunable local bosonic cutoff, which agrees with previous analytical and numerical results from [Phys. Rev. B 110, 235154 (2024)]. We show that the volume-law contribution to the entanglement entropy does not change upon breaking translational invariance via on-site disorder. We then numerically study the role of the subleading O(1) contribution to the entanglement entropy. We find that, in the particle-number conserving case, it exhibits a nontrivial dependence on the particle-number density and the local bosonic cutoff, while without particle-number conservation, results suggest the emergence of a universal O(1) contribution beyond the random pure state predictions.