Published: 2026-04-17 16:35:17
Authors: Reem Vitale, Masatoshi Hirabayashi
Categories: astro-ph.EP
Abstract:
Ceres, the dwarf planet in the main asteroid belt, hosts heavily cratered surfaces where craters are continuously eroded mainly due to impact bombardment with a limited influence by non-impact processes. Over continuous bombardment, such regions experience both crater production and erasure, eventually ceasing the crater population growth. This end-state, known as crater equilibrium, provides key information to constrain the mechanisms of crater degradation. The present study applies a recently extended crater equilibrium model to the crater equilibrium features and constrains the conditions for crater degradation on Ceres. We select eight heavily cratered sites as our test locations across four quadrangles (two sites per quadrangle) and collect crater counts using Dawn Framing Camera imagery. All sites exhibit cumulative size-frequency distributions (CSFD) with slopes slightly shallower than a power law of -2 at diameters below a few kilometers, strongly suggesting that the tested sites are at crater equilibrium. Our results show that the crater equilibrium state on Ceres resembles that on the Moon but is denser. Performing model fitting with crater counting data under negligible ejecta blanketing for crater erasure, we further show that crater degradation per single crater production on Ceres is comparable to or higher than that on the Moon. Combining this finding and the impact flux on Ceres, which is orders of magnitude higher than that on the Moon, suggests that crater degradation is much more elevated on Ceres than on the Moon, despite its denser crater population.
Published: 2026-04-17 16:28:40
Authors: Dorival Gonçalves, Ajay Kaladharan, Alberto Navarro
Categories: hep-ph, hep-ex, quant-ph
Abstract:
Angular correlations in Higgs decays to electroweak gauge bosons, $h \to ZZ^*, WW^*$, provide a powerful probe of both new physics effects and quantum information observables. We present a systematic study of semi-leptonic decays $h \to V V^* \to \ell^+\ell^- q\bar{q}$ and $\ell^\pm ν_\ell q\bar{q}'$, including finite final state fermion masses, NLO QCD, and NLO electroweak corrections. We show that finite final state quark masses can induce effects that go beyond the two-qutrit description in more inclusive regimes, while remaining controllable with suitable kinematic selections. QCD corrections lead to modest percent-level shifts, whereas electroweak corrections can significantly modify the angular structure, particularly in the $h\to ZZ^*$ channels. We assess the impact of these effects on the reconstructed density matrix and entanglement measures, finding that, while they modify the angular observables, semi-leptonic channels retain an effective two-qutrit description.
Published: 2026-04-17 16:28:31
Authors: Yanli Wang, Peng Kuang, Xiaoyu Han, Kaidi Xu, Haohan Wang
Categories: cs.CL, cs.AI
Abstract:
Large language models are increasingly deployed in settings where reliability matters, yet output-level uncertainty signals such as token probabilities, entropy, and self-consistency can become brittle under calibration--deployment mismatch. Conformal prediction provides finite-sample validity under exchangeability, but its practical usefulness depends on the quality of the nonconformity score. We propose a conformal framework for LLM question answering that uses internal representations rather than output-facing statistics: specifically, we introduce Layer-Wise Information (LI) scores, which measure how conditioning on the input reshapes predictive entropy across model depth, and use them as nonconformity scores within a standard split conformal pipeline. Across closed-ended and open-domain QA benchmarks, with the clearest gains under cross-domain shift, our method achieves a better validity--efficiency trade-off than strong text-level baselines while maintaining competitive in-domain reliability at the same nominal risk level. These results suggest that internal representations can provide more informative conformal scores when surface-level uncertainty is unstable under distribution shift.
Published: 2026-04-17 16:16:20
Authors: Vitalii Makogin, Evgeny Spodarev, Ilja Sukhanov
Categories: math.PR, math.ST
Abstract:
We propose a method for the prediction of stationary max--stable random fields with $α$-Fréchet marginal distribution $H_α$. The method is suitable to cope with heavy tails for $α\in(0,2)$ and is (approximately) exact in marginal distributions. It is based on a recent extrapolation approach via level sets which requires no moment assumptions. An explicit connection between the excursion metric and the Davis-Resnick distance is established. The existence of the predictor is proven. The non-uniqueness of the forecast is demonstrated on several examples. The method is tested on multiple simulated time series and random fields as well as applied to real data of annual maximum precipitation.
Published: 2026-04-17 16:04:17
Authors: Jun Chen, Thomas Wiegelmann, Li Feng, Chaowei Jiang, Rui Liu
Categories: astro-ph.SR
Abstract:
We present a new version of FastQSL for locating quasi-separatrix layers (QSLs) -- regions characterized by strong magnetic connectivity gradients, preferential current buildup, and subsequent magnetic reconnection. This version now supports spherical coordinates, utilizing a second spherical coordinate system for tracing magnetic field lines around the polar regions. This approach completely resolves the singularity problem at the two poles. Furthermore, our code accommodates arbitrary mesh shapes for output, can provide both magnetic field and electric current density on the mesh, and can save the traced magnetic field lines. We suggest using $Q_\mathrm{local}$ calculated through a localized mapping to locate (quasi-)separators. By quickly and accurately outputting the footpoint coordinates of magnetic field lines, FastQSL can be used to derive the two key parameters used for modeling solar wind speed and slip-squashing factors for the case of zero boundary flow. Compared with the first version, FastQSL 2 achieves significant improvements in terms of application scope.
Published: 2026-04-17 16:01:54
Authors: Leo Ferres, Laetitia Gauvin
Categories: physics.soc-ph
Abstract:
Call detail records (CDR) from mobile phone networks are widely used to study human mobility however CDR data from a single mobile operator are inherently biased because the observed users do not mirror the population distribution. Using data from a major Chilean carrier in Santiago, we observe the user base is skewed by socioeconomic group, so aggregate metrics like radius of gyration are distorted by the population that is actually observed.
To correct this sampling bias, we apply multilevel regression and poststratification (MRP), a method that is not yet standard for CDR-based mobility studies. We fit a Bayesian multilevel model for individual mobility using socioeconomic status, gender, and geography, with partial pooling across comunas, and then poststratify the predictions to match census demographics. This approach reduces the naive CDR estimate of average radius of gyration by about 17%.
Importantly, a version of the model that uses only geographic information still captures much of the bias, showing that MRP can be useful even when the socioeconomic composition of users is not fully known, as long as spatial patterns of socioeconomic groups exist. This example demonstrates how MRP can provide a principled correction for non-representative CDR-derived mobility estimates, rather than treating the carrier sample as if it were a random population sample.
Published: 2026-04-17 15:59:51
Authors: Anne S. Freise, Jamie I. McDonald, Kirill Riabtsev, Samuel J. Witte
Categories: astro-ph.HE
Abstract:
The high-frequency gravitational-wave band is often discussed primarily in the context of new physics, but realistic Standard-Model foregrounds remain incompletely characterized. We investigate pulsar polar caps as a physically motivated astrophysical source of high-frequency gravitational waves, generated by repeated discharge cycles in compact near-surface plasma gaps. Our baseline result is population-level: we construct the signal from the Galactic normal-pulsar population rather than from a single especially favorable object. To do so, we calibrate the source dynamics with particle-in-cell simulations performed at real physical scales, with physical pulsar parameters mapped directly onto numerical scales, and then lift the resolved longitudinal discharge to a cap-scale emission model. The gravitational-wave signal is computed in a full Fourier-space framework, retaining finite-source, geometric, and polarization effects explicitly. Within this treatment, the dominant contribution is not the purely electric channel emphasized in some earlier simplified approaches, but a source channel involving the large background magnetic field and discharge-induced transverse fluctuations of magnetic field. Integrating this description over a normal-pulsar population, we find an astrophysical foreground in the MHz-scale high-frequency band that can overlap with and partially obscure the thermal gravitational-wave signal sourced by the plasma of the early Universe. At the same time, the normalization remains sensitive to the modeled assumptions. Although the predicted strain remains far below current experimental sensitivity, pulsar polar caps provide a concrete Standard-Model foreground benchmark in a band often treated as nearly background-free. Alternative source configurations further broaden the plausible signal range around this baseline.
Published: 2026-04-17 15:55:38
Authors: Timo Wagner, Michael Himpel, Thomas Ihle, Horst-Holger Boltz
Categories: cond-mat.soft, cond-mat.stat-mech
Abstract:
Active matter systems characterized by the interplay of chirality and self-alignment offer a rich landscape for the emergence of non-equilibrium collective behaviors and the development of autonomous materials. We present a versatile experimental platform for studying these dynamics using augmented commercial bristlebots, where custom-designed housings and elastic couplings induce a self-aligning torque and a stable chiral drift. By mapping experimental trajectories to a Langevin-type model, we characterize the single-particle dynamics. In circular geometries, we show that the stability of edge currents is governed by the interaction between intrinsic particle chirality and handedness of the edge current. Furthermore, we demonstrate that transport can be geometrically rectified using a nautilus-shaped obstacle, which acts as a doubly chirality-sensitive ratchet. Finally, we explore the collective dynamics of rigidly linked assemblies, observing spontaneous mode-switching between translational and rotational states in triangular active solids. Our results provide a robust framework for the passive control of active gases and illustrate how geometric constraints can be used to program complex transport properties in synthetic active systems.
Published: 2026-04-17 15:38:37
Authors: Alexandra Dragomir, Ioana Pintilie, Antonio Barbalau, Marius Dragoi, Florin Brad, Cristian Daniel Paduraru, Alexandru Tifrea, Elena Burceanu, Radu Tudor Ionescu
Categories: cs.LG, cs.AI, cs.CL
Abstract:
Adapter-based methods have become a cost-effective approach to continual learning (CL) for Large Language Models (LLMs), by sequentially learning a low-rank update matrix for each task. To mitigate catastrophic forgetting, state-of-the-art approaches impose constraints on new adapters with respect to the previous ones, by targeting either subspace or coordinate-wise interference. In this paper, we propose JumpLoRA, a novel framework to adaptively induce sparsity in the Low-Rank Adaptation (LoRA) blocks through the use of JumpReLU gating. The method achieves dynamic parameter isolation, which helps prevent task interference. We demonstrate that our method is highly modular and compatible with LoRA-based CL approaches. Specifically, it significantly boosts the performance of IncLoRA and outperforms the leading state-of-the-art CL method, ELLA.
Published: 2026-04-17 15:35:43
Authors: Giuseppe Puglisi, Avinash Anand, Marina Migliaccio
Categories: astro-ph.GA, astro-ph.CO, astro-ph.IM
Abstract:
We introduce an innovative approach employing Cycle Generative Adversarial Networks (Cycle-GANs) to accurately simulate Carbon Monoxide (CO) emissions by learning features identified in thermal dust emission maps from the Planck satellite alongside HI data from HI4PI survey. Our training dataset is complemented by the targets represented by the two rotational transition lines of CO (J:1-0, J:2-1) provided by the Planck satellite. We ensure the robustness of our dataset by focusing on regions with a signal-to-noise ratio (SNR) exceeding 8. The outcomes, assessed utilizing angular power spectra and Minkowski functionals, confirm that our algorithm proficiently achieves the set goals, indicating that the amplitudes of the generated emission accurately reproduce the angular correlations and share the statistical properties of the employed CO targets. We thus aim at improving the current models of CO emission specifically in the high-Galactic latitude areas that have been hardly observed by the most recent surveys, and, in doing so, to address and overcome the limitations affecting current models regions. This research lays the groundwork for creating transformative synthetic simulations, leveraging convolutional neural networks tied to data procured from latest observations.
Published: 2026-04-17 15:35:33
Authors: Peter F. Wyper, Jonathan Squire, Etienne Pariat, Oleksiy V. Agapitov, Jim F. Drake, Norbert Magyar, William H. Matthaeus, Lorenzo Matteini, David Ruffolo, Victor Réville, Chen Shi, Munehito Shoda, Marc Swisdak, Marco Velli, Mojtaba Akhavan-Tafti, Bahaeddine Gannouni, Roberto Lionello, Maria S. Madjarska, Mathew J. Owens, Nour E. Rawafi, Alphonse C. Sterling, Durgesh Tripathi
Categories: astro-ph.SR, physics.plasm-ph, physics.space-ph
Abstract:
Magnetic switchbacks are large amplitude deflections of the magnetic field within the solar wind. They are Alfvénic in character and so are associated with a spike in velocity and a generally small variation in local plasma density. Early orbits of Parker Solar Probe revealed that the solar wind near the Sun is dominated by these structures, and therefore, they may be playing an important role in the energy budget and acceleration of the young solar wind. In this review, we present an overview of different mechanisms that have been proposed for how switchbacks could be formed. We group the mechanisms by whether they predominantly act in the low solar atmosphere or within the solar wind (in situ). We focus on mechanisms that can create reversals of the ambient magnetic field direction and, thus, account for the most extreme perturbations. The general consensus is that mechanisms in the lower solar atmosphere do not form such reversals on their own but provide the seed perturbations, flows, or particle beams necessary for in situ mechanisms to create switchbacks within the solar wind. Switchback observations thus likely contain an imprint of the coronal source of the seed perturbation or flow, which is evolved further locally by one of several plausible in situ mechanisms. We discuss the strengths and weaknesses of each mechanism and outline future observational and theoretical tests that could help differentiate between them.
Published: 2026-04-17 15:34:18
Authors: Long Xiong, Xiaoyang Wang, Xiaoxia Cai, Xiao Yuan
Categories: quant-ph
Abstract:
Nonlinear spectroscopy is a cornerstone of quantum science, providing unique access to multi-point correlations, quantum coherence, and couplings that are invisible to linear methods. However, classical simulation of these phenomena is fundamentally limited by the exponential growth of the Hilbert space, and practical quantum algorithms for the nonlinear regime have remained largely unexplored. Here, we present a unified quantum algorithmic framework for computing $n$-th order nonlinear spectroscopies. By reformulating multi-time responses as a weighted sum of expectation values at finite pump amplitudes via a generalized parameter shift rule, our approach bypasses the costly evaluation of high-order commutators and time-dependent operator expansions. This reformulation enables efficient execution via real-time evolution on current quantum hardware, ensuring inherent noise resilience. We validate the framework on IBM's superconducting quantum processors, successfully obtain higher-order response functions of a 12-qubit XXZ spin-chain. Furthermore, the versatility of our method is demonstrated by resolving quasi-particle excitation spectra in spin-liquids and identifying interaction-induced cross-peaks in atomic systems. Our results establish a practical and scalable pathway for probing complex quantum dynamics on near-term quantum devices, extending the reach of quantum simulation into the nonlinear domain.
Published: 2026-04-17 15:30:24
Authors: Marie-Noëlle Célérier
Categories: astro-ph.CO
Abstract:
The purpose of the present work is based on two main observations: the tensions encountered by the standard $Λ$CDM model when confronted to precision small scale cosmological data and the finding that the matter distribution and the expansion of the Universe are axially symmetric roughly in the direction of the CMB dipole. Therefore, we propose, as a model for the inhomogeneous local universe, an axially symmetric Szekeres solution. After describing its main properties, we are left with three metric functions to be fitted to data between the observer and the transition to homogeneity which is an intrinsic feature of Szekeres spacetimes. So as to turn a difficult functional inference problem into a classical parameter estimation problem, we propose to use Chebyshev polynomial expansions, which, as a first step, we truncate after the second order terms. We are thus left with eight constant parameters: six for the metric functions, plus the observer's radial location and the cosmological constant. Here are the proper ingredients needed to implement the data fitting to the model in the future.
Published: 2026-04-17 15:23:30
Authors: Simone Heisinger, Luca Pulina, Martina Seidl
Categories: cs.LO
Abstract:
The QBF Gallery 2023, the last QBF evaluation event, continues the tradition to survey and document the state of the art in solving quantified Boolean formulas (QBFs). It provides a detailed overview by collecting newly developed solvers and formulas as benchmarks.
This report documents the solvers and formulas submitted by the community and introduces a new, consolidated benchmark set that combines well-evaluated formulas with the submitted instances. The resulting formula set is made publicly available. With this benchmark set, we conduct a comparative analysis of the submitted solvers and publicly available solvers, assessing their performance and current capabilities. In addition, we report on the present status of the QBF Gallery and discuss ideas and directions for future editions to further support research and benchmarking within the QBF community.
Published: 2026-04-17 15:20:13
Authors: Ayoub Hammal, Pierre Zweigenbaum, Caio Corro
Categories: cs.CL
Abstract:
Recent works proposed test-time alignment methods that rely on a small aligned model as a proxy that guides the generation of a larger base (unaligned) model. The implicit reward approach skews the large model distribution, whereas the nudging approach defers the generation of the next token to the small aligned model when the large base one is unconfident about its outcome. In this work, we first show that both approaches can be reduced to sampling from similar graphical models, where they differ only in the definition of a rejection criterion (or distribution). Moreover, we argue that the confidence criterion is ill-motivated due to linguistic phenomena like ambiguous phrasing. We propose a novel rejection criterion based on a conservative confidence bet. Experimentally, our novel approach outperforms previous work on several datasets.
Published: 2026-04-17 15:18:01
Authors: Minchul Kang, Changyong Shin, Jinwoo Jeong, Hyunho Lee, Younghun Go, Gyeongmin Kim, Gyeongsik Yang, Chuck Yoo
Categories: cs.LG, cs.AI, cs.DC, cs.PF
Abstract:
Accurate prediction of training time in distributed deep learning is crucial for resource allocation, cost estimation, and job scheduling. We observe that the floating-point precision setting is a key determinant of training time, leading to training time variations of ~2.4x over its minimum. However, existing studies on distributed training time prediction rely on static model computation graphs that do not capture precision variations, including mixed precision. According to our experiments, training time prediction without considering precision results in significant prediction errors - reaching up to 147.85% in mean absolute percentage error (MAPE). To address this issue, we propose a precision-aware distributed training time predictor that achieves robust accuracy across diverse precision settings, including mixed precision, with 9.8% MAPE.
Published: 2026-04-17 15:02:01
Authors: Luca Ferrari, Billel Habbati, Meriem Guerar, Mariano Ceccato, Luca Verderame
Categories: cs.CR
Abstract:
Mobile application developers are required to disclose how they collect, use, and share user data in compliance with privacy regulations. To support transparency, major app marketplaces have introduced standardized disclosure mechanisms. In 2022, Google mandated the Data Safety Section (DSS) on Google Play, requiring developers to summarize their data practices. However, compiling accurate DSS disclosures is challenging, as they must remain consistent with the corresponding privacy policy (PP), and no automated tool currently verifies this alignment. Prior studies indicate that nearly 80% of popular apps contain incomplete or misleading DSS declarations. We present PolicyGapper, an LLM-based methodology for automatically detecting discrepancies between DSS disclosures and privacy policies. PolicyGapper operates in four stages: scraping, pre-processing, analysis, and post-processing, without requiring access to application binaries. We evaluate PolicyGapper on a dataset of 330 top-ranked apps spanning all 33 Google Play categories, collected in Q3 2025. The approach identifies 2,689 omitted disclosures, including 2,040 related to data collection and 649 to data sharing. Manual validation on a stratified 10% subset, repeated across three independent runs, yields an average Precision of 0.75, Recall of 0.77, Accuracy of 0.69, and F1-score of 0.76. To support reproducibility, we release a complete replication package, including the dataset, prompts, source code, and results available at https://github.com/Mobile-IoT-Security-Lab/PolicyGapper and https://doi.org/10.5281/zenodo.19628493.
Published: 2026-04-17 14:56:29
Authors: Xibo Li, Liang Zhang
Categories: cs.IR
Abstract:
Test-time augmentation (TTA) has become a promising approach for mitigating data sparsity in sequential recommendation by improving inference accuracy without requiring costly model retraining. However, existing TTA methods typically rely on uniform, user-agnostic augmentation strategies. We show that this "one-size-fits-all" design is inherently suboptimal, as it neglects substantial behavioral heterogeneity across users, and empirically demonstrate that the optimal augmentation operators vary significantly across user sequences with different characteristics for the first time. To address this limitation, we propose AdaTTA, a plug-and-play reinforcement learning-based adaptive inference framework that learns to select sequence-specific augmentation operators on a per-sequence basis. We formulate augmentation selection as a Markov Decision Process and introduce an Actor-Critic policy network with hybrid state representations and a joint macro-rank reward design to dynamically determine the optimal operator for each input user sequence. Extensive experiments on four real-world datasets and two recommendation backbones demonstrate that AdaTTA consistently outperforms the best fixed-strategy baselines, achieving up to 26.31% relative improvement on the Home dataset while incurring only moderate computational overhead
Published: 2026-04-17 11:32:20
Authors: Florin Felix Nichita
Categories: math.HO
Abstract:
I was interested in the work of Solomon Marcus in Mathematical Linguistics as a high-school student. Later, I had the opportunity to discuss with him about many topics. He was a polymath. We wrote a paper together, and I refereed an editorial paper about his work in 2021. Samples of (possible) discussions are presented: some topology conjectures, a self-dual theorem in geometry, results about Boolean algebras, a B-ring Euler formula, Yang-Baxter maps and a discussion on sequences and series. A short appendix on poetry is also included.
Published: 2026-04-17 11:30:46
Authors: Chaoshuo Zhang, Yibo Liang, Mengke Tian, Chenhao Lin, Zhengyu Zhao, Le Yang, Chong Zhang, Yang Zhang, Chao Shen
Categories: cs.CR, cs.CV
Abstract:
Despite the remarkable synthesis capabilities of text-to-image (T2I) models, safeguarding them against content violations remains a persistent challenge. Existing safety alignments primarily focus on explicit malicious concepts, often overlooking the subtle yet critical risks of compositional semantics. To address this oversight, we identify and formalize a novel vulnerability: Multi-Concept Compositional Unsafety (MCCU), where unsafe semantics stem from the implicit associations of individually benign concepts. Based on this formulation, we introduce TwoHamsters, a comprehensive benchmark comprising 17.5k prompts curated to probe MCCU vulnerabilities. Through a rigorous evaluation of 10 state-of-the-art models and 16 defense mechanisms, our analysis yields 8 pivotal insights. In particular, we demonstrate that current T2I models and defense mechanisms face severe MCCU risks: on TwoHamsters, FLUX achieves an MCCU generation success rate of 99.52%, while LLaVA-Guard only attains a recall of 41.06%, highlighting a critical limitation of the current paradigm for managing hazardous compositional generation.
Published: 2026-04-17 11:26:15
Authors: Haitao Yang, Ruiqi Hu, Heng Wu, Xiaolong He, Yan Zhou, Yizhe Xue, Kexin He, Wenshuai Hu, Haosen Chen, Mingming Gong, Xin Zhang, Ping-Heng Tan, Eduardo R Hernández, Yong Xie
Categories: cond-mat.mtrl-sci
Abstract:
Two-dimensional materials are expected to play an important role in next-generation electronics and optoelectronic devices. Recently, twisted bilayer graphene and transition metal dichalcogenides have attracted significant attention due to their unique physical properties and potential applications. In this study we describe the use of optical microscopy to collect the color space of chemical vapor deposition (CVD) molybdenum disulfide ($\mbox{MoS}_2$), and the application of a semantic segmentation convolutional neural network (CNN) to accurately and rapidly identify thicknesses of $\mbox{MoS}_2$ flakes. A second CNN model is trained to provide precise predictions on the twist angle of CVD-grown bilayer flakes. This model harnessed a dataset comprising over 10,000 synthetic images, encompassing geometries spanning from hexagonal to triangular shapes. Subsequent validation of the deep learning predictions on twist angles was executed through the second harmonic generation and Raman spectroscopy. Our results introduce a scalable methodology for automated inspection of twisted atomically thin CVD-grown bilayer.
Published: 2026-04-17 11:24:56
Authors: Yaohong Yang, Sammie Katt, Samuel Kaski
Categories: cs.LG
Abstract:
Multi-objective Bayesian optimization (MOBO) provides a principled framework for optimizing expensive black-box functions with multiple objectives. However, existing MOBO methods often struggle with coverage, scalability with respect to the number of objectives, and integrating constraints and preferences. In this work, we propose \textit{STAGE-BO, Sequential Targeting Adaptive Gap-Filling $\varepsilon$-Constraint Bayesian Optimization}, that explicitly targets under-explored regions of the Pareto front. By analyzing the coverage of the approximate Pareto front, our method identifies the largest geometric gaps. These gaps are then used as constraints, which transforms the problem into a sequence of inequality-constrained subproblems, efficiently solved via constrained expected improvement acquisition. Our approach provides a uniform Pareto coverage without hypervolume computation and naturally applies to constrained and preference-based settings. Experiments on synthetic and real-world benchmarks demonstrate superior coverage and competitive hypervolume performance against state-of-the-art baselines.
Published: 2026-04-17 11:23:59
Authors: Andreea-Elena Bodea, Stephen Meisenbacher, Florian Matthes
Categories: cs.CR, cs.CL
Abstract:
Despite the considerable promise of Retrieval-Augmented Generation (RAG), many real-world use cases may create privacy concerns, where the purported utility of RAG-enabled insights comes at the risk of exposing private information to either the LLM or the end user requesting the response. As a potential mitigation, using anonymization techniques to remove personally identifiable information (PII) and other sensitive markers in the underlying data represents a practical and sensible course of action for RAG administrators. Despite a wealth of literature on the topic, no works consider the placement of anonymization along the RAG pipeline, i.e., asking the question, where should anonymization happen? In this case study, we systematically and empirically measure the impact of anonymization at two important points along the RAG pipeline: the dataset and generated answer. We show that differences in privacy-utility trade-offs can be observed depending on where anonymization took place, demonstrating the significance of privacy risk mitigation placement in RAG.
Published: 2026-04-17 11:23:17
Authors: Xhevahire Tërnava, Georges Aaron Randrianaina, Luc Lesoil, Mathieu Acher
Categories: cs.SE
Abstract:
Many small-scale software systems, that is, with limited codebase or binary size, are widely used in everyday tasks, yet their configurability remains largely unexplored. At the same time, studies on modern software systems show a trend toward increasing configurability, alongside growing interest in building immutable, specialized, and reproducible software. In this paper, we present the first empirical study on the extent of configurability in small-scale software systems. By analyzing 108 programs from GNU coreutils, we show that even small programs can exhibit significant compile-time and run-time variability, with up to 76 options per program. Then, there is a high correlation (0.78) between run-time variability and codebase size. Furthermore, an analysis of the 20 smallest programs across 85 releases reveals that variability tends to increase over time, primarily due to the added compile-time variability. This suggests that shifting options between run-time and compile-time, removing unnecessary run-time variability, or resolving compile-time variability early, can help reduce codebase complexity and size. We also introduce, for the first time, the concept of null-variable software system, one with no configurability beyond mandatory features. Our findings show that high configurability is not exclusive to large-scale systems and that reducing unnecessary variability can lead to lightweight, smaller, and more maintainable software. We hope this effort contributes to designing new software by understanding how to balance its configurability with codebase size.
Published: 2026-04-17 11:17:38
Authors: Leo Bürk, Artjom Hermann, Markus Weyrauch, Markus Uhlmann
Categories: physics.flu-dyn
Abstract:
Particle-resolved direct numerical simulation (PR-DNS) is performed for turbulent open channel flow over a smooth horizontal wall with a vertical cylinder and a dilute set of mobile, heavy, spherical particles. At the chosen parameter point (which matches a previous study without a cylinder) the particles are mostly translating in the horizontal plane while remaining in contact with the wall. It is shown that the presence of the cylinder leads to the generation of intense vortical structures, enhanced turbulence intensity in the wake region, and to strong modifications of the local wall shear stress. These cylinder-induced perturbations have direct consequences for the average particle concentration: preferential accumulation/depletion in different parts of the wake region occurs, while the wall-normal transport of particles (against gravity) is significantly enhanced. A second simulation which adds roughness elements on the wall reveals an additional effect upon the wall-normal distribution of particles. It turns out that the configuration with wall-roughness and a wall-mounted cylinder features the largest fraction of entrained particles, even far from the wall.
Published: 2026-04-17 11:12:55
Authors: Hamed Jelodar, Samita Bai, Mohammad Meymani, Parisa Hamedi, Roozbeh Razavi-Far, Ali Ghorbani
Categories: cs.AI
Abstract:
Generative AI, particularly Large Language Models, increasingly integrates graph-based representations to enhance reasoning, retrieval, and structured decision-making. Despite rapid advances, there remains limited clarity regarding when, why, where, and what types of graph-LLM integrations are most appropriate across applications. This survey provides a concise, structured overview of the design choices underlying the integration of graphs with LLMs. We categorize existing methods based on their purpose (reasoning, retrieval, generation, recommendation), graph modality (knowledge graphs, scene graphs, interaction graphs, causal graphs, dependency graphs), and integration strategies (prompting, augmentation, training, or agent-based use). By mapping representative works across domains such as cybersecurity, healthcare, materials science, finance, robotics, and multimodal environments, we highlight the strengths, limitations, and best-fit scenarios for each technique. This survey aims to offer researchers a practical guide for selecting the most suitable graph-LLM approach depending on task requirements, data characteristics, and reasoning complexity.
Published: 2026-04-17 11:09:18
Authors: David Lafontaine, Camille Laurent
Categories: math.AP
Abstract:
We prove scattering for the defocusing energy-critical non-linear wave equation with Dirichlet boundary conditions outside two strictly convex obstacles in dimension three. This is the first large data scattering result for such an equation in the presence of trapped trajectories. Our result is in fact more general and can be used as a black box in other geometries. More precisely, under the assumptions that the corresponding linear wave equation satisfies global Strichartz estimates, that the domain is weakly non-trapping and that trajectories do not reconcentrate, we show linear and nonlinear profile decompositions in infinite time. This implies scattering under the rigidity assumption that the only compact-flow solution is the trivial one.
Published: 2026-04-17 10:59:49
Authors: Khang Le, Joaquín Torres-Sospedra, Philipp Müller
Categories: cs.LG, stat.AP
Abstract:
Fixed Radius Near Neighbor (FRNN) search is an alternative to the widely used k Nearest Neighbors (kNN) search. Unlike kNN, FRNN determines a label or an estimate for a test sample based on all training samples within a predefined distance. While this approach is beneficial in certain scenarios, assuming a fixed maximum distance for all training samples can decrease the accuracy of the FRNN. Therefore, in this paper we propose the Adaptive Radius Near Neighbor (ARNN) and the Weighted ARNN (WARNN), which employ adaptive distances and in latter case weights. All three methods are compared to kNN and twelve of its variants for a regression problem, namely WiFi fingerprinting indoor positioning, using 22 different datasets to provide a comprehensive analysis. While the performances of the tested FRNN and ARNN versions were amongst the worse, three of the four best methods in the test were WARNN versions, indicating that using weights together with adaptive distances achieves performance comparable or even better than kNN variants.
Published: 2026-04-17 10:21:46
Authors: Jan Niklas Schmäke, Oliver Wege, Martin Ruess
Categories: math.NA
Abstract:
An extension of the multi-level hp Finite Cell Method is proposed for the simulation of thermoviscoplastic problems with temperature-dependent material behavior. The approach combines hierarchical adaptive refinement with a non-negative moment fitting (NNMF) quadrature scheme for efficient and robust integration of non-linear, history-dependent constitutive models on cut cells. The NNMF formulation yields sparse, positive quadrature rules that significantly reduce the number of integration points while maintaining stability and accuracy. An error-indicator-driven hp-refinement strategy enables localized resolution of strain and thermal gradients during the non-linear solution process. The framework is implemented within a partitioned thermo-mechanical scheme and evaluated on benchmark and application-oriented examples. The results demonstrate improved accuracy and substantial computational savings compared to standard integration approaches.
Published: 2026-04-17 10:21:32
Authors: Szilárd Szalay, Frédéric Holweck
Categories: quant-ph, math-ph
Abstract:
We present two general methods to implement quantum circuits for the direct measuring of local unitary invariants on quantum computers. We work these out for important three-qubit invariants, and also demonstrate these on the IBM Quantum Platform for important entanglement measures of three qubits.
Published: 2026-04-17 10:11:39
Authors: Shitong Shao, Lichen Bai, Pengfei Wan, James Kwok, Zeke Xie
Categories: cs.CV
Abstract:
Video diffusion models have rapidly become the dominant paradigm for high-fidelity generative video synthesis, but their practical deployment remains constrained by severe inference costs. Compared with image generation, video synthesis compounds computation across spatial-temporal token growth and iterative denoising, making attention and memory traffic major bottlenecks in real-world settings. This survey provides a systematic and deployment-oriented review of efficient video diffusion models. We propose a unified categorization that organizes existing methods into four classes of main paradigms, including step distillation, efficient attention, model compression, and cache/trajectory optimization. Building on this categorization, we respectively analyze algorithmic trends of these four paradigms and examine how different design choices target two core objectives: reducing the number of function evaluations and minimizing per-step overhead. Finally, we discuss open challenges and future directions, including quality preservation under composite acceleration, hardware-software co-design, robust real-time long-horizon generation, and open infrastructure for standardized evaluation. To the best of our knowledge, our work is the first comprehensive survey on efficient video diffusion models, offering researchers and engineers a structured overview of the field and its emerging research directions.
Published: 2026-04-17 10:10:49
Authors: Ullas Chandran S. V., Gabriele Di Stefano, Grahame Erskine, Haritha S, Elias John Thomas, James Tuite
Categories: math.CO
Abstract:
The general position number for graphs ask for largest vertex subsets $S$ such that no three vertices are contained on a common shortest path. We examine this problem in the setting of directed graphs. We provide bounds for the general position number of digraphs, show that the problem is NP-complete for oriented graphs, investigate the problem for some important families of digraphs such as circulant digraphs, Kautz digraphs and permutation digraphs, and study the general position numbers obtained from all orientations of an undirected graph.
Published: 2026-04-17 10:04:25
Authors: Shungo Aoyagi, Shunsuke Kitou, Yuiga Nakamura, Taka-hisa Arima, Naoya Kanazawa
Categories: cond-mat.str-el, cond-mat.mtrl-sci
Abstract:
Symmetry classification of crystal structures has been central to predicting physical properties of materials. While such structural classification identifies which physical responses are symmetry-allowed, the magnitudes of these responses are governed by the degree of symmetry breaking in the electronic state. However, a well-defined quantitative descriptor for the electronic symmetry breaking has been established only in limited cases such as electric polarization and magnetization. No analogous descriptor exists for most other types, including chirality. Here, we propose an experimental framework for quantifying electronic symmetry breaking from the anisotropy of valence electron density distribution. We show that the orbital hybridization phases governing this anisotropy can be uniquely determined under site symmetry constraints. Applying this framework to structurally chiral transition-metal silicides, we determine hybridization phases from their valence electron densities observed by synchrotron X-ray diffraction. From the obtained complex hybridization, we quantify an electronic chirality $χ$ and theoretically demonstrate that it is directly proportional to circular dichroism, establishing $χ$ as a predictive descriptor of chiral responses. This approach is systematically applicable to various point groups, offering a general route to quantifying electronic symmetry breaking and predicting associated physical properties.
Published: 2026-04-17 09:39:36
Authors: Vishal Ramesh, Antony Thomas
Categories: cs.RO
Abstract:
Multi-UAV inspection missions require spare drones to replace active drones during recharging cycles. Existing fleet-sizing approaches often assume steady-state operating conditions that do not apply to finite-horizon missions, or they treat replacement requests as statistically independent events. The latter provides per-request blocking guarantees that fail to translate to mission-level reliability when demands cluster. This paper identifies a structural failure mode where efficient routing assigns similar workloads to each UAV, leading to synchronized battery depletion and replacement bursts that exhaust the spare pool even when average capacity is sufficient.
We derive a closed-form sufficient fleet-sizing rule, k = m(ceil(R) + 1), where m is the number of active UAVs and R is the recovery-to-active time ratio. This additive buffer of m spares absorbs worst-case synchronized demand at recovery-cycle boundaries and ensures mission-level reliability even when all UAVs deplete simultaneously. Monte Carlo validation across five scenarios (m in [2, 10], R in [0.87, 3.39], 1000 trials each) shows that Erlang-B sizing with a per-request blocking target epsilon = 0.01 drops to 69.9% mission success at R = 3.39, with 95% of spare exhaustion events concentrated in the top-decile 5-minute demand windows. In contrast, the proposed rule maintains 99.8% success (Wilson 95% lower bound 99.3%) across all tested conditions, including wind variability up to CV = 0.30, while requiring only four additional drones in the most demanding scenario.
Published: 2026-04-17 09:26:43
Authors: Ya-Guang Wang, Yi-Lei Zhao
Categories: math.AP
Abstract:
This paper is devoted to the study of the compressible boundary layer equations in the Gevrey-2 solution space. Compared to the classical Prandtl equation, the additional complexity arises from the strong interaction between viscous layer and thermal layer. By introducing new auxiliary functions and observing the cancellation mechanism to overcome the loss of derivatives, we show the local existence and uniqueness of the solution in the Gevrey-2 space in the tangential variable and Sobolev regularity in the normal variable by using a direct energy method.
Published: 2026-04-17 09:22:28
Authors: Kush Kinra
Categories: math.PR, math.AP
Abstract:
Continuous data assimilation (CDA) techniques, most notably the nudging approach proposed by Azouani, Olson, and Titi (AOT), have been shown to be very successful in deterministic frameworks for achieving long-time synchronization between an approximate state and true state. In this note, we develop and study a CDA scheme for a class of stochastic non-Newtonian fluids, namely third-grade fluids, subject to either additive or multiplicative Gaussian stochastic forcing in both two- and three-dimensional settings. We establish sufficient criteria on the nudging gain and the observational mesh size that guarantee convergence of the assimilated state toward the underlying stochastic solution. Convergence is proved in the mean-square sense, and, in the case of additive noise, we further obtain almost sure (pathwise) convergence.
Published: 2026-04-17 09:20:55
Authors: Eñaut Mendiluze Usandizaga, Thomas Laurent, Paolo Arcaini, Shaukat Ali
Categories: cs.SE, cs.DB
Abstract:
Quantum software testing has attracted interest in recent years, prompting the development of various techniques to automate the testing of quantum software. These techniques generate test cases that must be assessed for their effectiveness in detecting faults. Such an assessment requires benchmarks of faulty programs. However, there is a lack of benchmarks containing faults. In this data showcase, we propose QMutBench, a dataset that contains over 700,000 quantum circuit mutants representing different faults. The dataset is accessible via an online interface with selection criteria, such as the original quantum circuit(s) from which mutants are generated, the desired survival rate of the selected mutants, and other mutation characteristics (e.g., the type of faulty quantum gate). QMutBench provides quantum software developers and testers with an accessible online dataset to obtain benchmarks of mutants necessary to assess either the quality of the test cases generated by their testing technique or to compare different testing techniques. It also enables the development of new mutation-guided quantum software testing techniques.
Published: 2026-04-17 09:18:39
Authors: Xuanming Huang, Chi Ding, Yujie Sun, Shidi Huang, Andrea Cioncolini, Damiano Padovani, Ju Liu
Categories: physics.flu-dyn
Abstract:
This study presents a systematic validation and comparative assessment of computational fluid dynamics (CFD) strategies for centrifugal blood pump simulations using the U.S. Food and Drug Administration benchmark model. A scale-resolving large eddy simulation (LES) with transient sliding-interface (SI) coupling is evaluated and compared against Reynolds-averaged Navier-Stokes (RANS) approaches employing both multiple reference frame and SI formulations. Numerical predictions are validated through direct comparison with particle image velocimetry measurements under two representative operating conditions. The results indicate that LES with transient rotor-stator coupling achieves consistently improved agreement with experimental velocity fields compared with RANS-based methods, particularly in the diffuser region where strong intermittency and wall-bounded turbulence are present. In contrast, RANS-based approaches exhibit noticeable discrepancies in these regions. A mesh sensitivity study and an assessment of temporal averaging effects are conducted for LES. The quality of the LES results is further quantified using three complementary metrics, demonstrating that a mesh resolution of approximately 80 million cells achieves a well-resolved LES regime. Building on the validated scale-resolving simulations, detailed analyses of vortical structures, turbulent kinetic energy distributions, and velocity energy spectra are performed to characterize the internal flow physics of the pump. This study demonstrates that scale-resolving, transient simulation approaches are essential for accurately capturing the highly unsteady, turbulence-dominated flow features in ventricular assist devices and provides practical guidance for future high-fidelity hemodynamic and hemocompatibility studies.
Published: 2026-04-17 09:13:52
Authors: Zhi Zhang, Chalermchon Satirapod, Bingtao Ma, Changjun Gu
Categories: cs.RO
Abstract:
Solid-state LiDAR-inertial SLAM has attracted significant attention due to its advantages in speed and robustness. However, achieving accurate mapping in extreme environments remains challenging due to severe geometric degeneracy and unreliable observations, which often lead to ill-conditioned optimization and map inconsistencies. To address these challenges, we propose an environment-adaptive solid-state LiDAR-inertial odometry that integrates local normal-vector constraints with degeneracy-aware map maintenance to enhance localization accuracy. Specifically, we introduce local normal-vector constraints to improve the stability of state estimation, effectively suppressing localization drift in degenerate scenarios. Furthermore, we design a degeneration-guided map update strategy to improve map precision. Benefiting from the refined map representation, localization accuracy is further enhanced in subsequent estimation. Experimental results demonstrate that the proposed method achieves superior mapping accuracy and robustness in extreme and perceptually degraded environments, with an average RMSE reduction of up to 12.8% compared to the baseline method.
Published: 2026-04-17 09:10:28
Authors: Angela Capocefalo, Francesco Brasili, Javier Pérez, Edouard Chauveau, Stefano Casciardi, Andrea Militello, Francesco Sciortino, Emanuela Zaccarelli, Federico Bordi, Domenico Truzzolillo, Simona Sennato
Categories: cond-mat.soft
Abstract:
Plasmonic nanoparticles (NPs) integrated within thermoresponsive polymeric microgels provide a versatile platform for the realization of stimuli-responsive optical materials, where the microgel volume phase transition enables dynamic control of plasmon coupling. This study uncovers a counter-intuitive re-entrant behavior with increasing NP loading in which plasmon coupling initially strengthens and subsequently weakens beyond a critical NP-to-microgel number ratio. By combining light and X-ray scattering techniques with optical spectroscopy and electrophoretic mobility measurements, it is demonstrated that plasmon coupling is governed not only by the interparticle distance between NPs confined within individual microgels, but also by the colloidal stability of the hybrid complexes. At intermediate NP loadings, surface charge inhomogeneities induced by NP adsorption promote aggregation of microgel-NPs complexes, resulting in enhanced plasmon coupling. In contrast, when the complexes remain colloidally stable, coupling is dictated solely by NP organization within the corona of individual microgels. A quantitative relationship between plasmon coupling and interparticle distance reveals two distinct coupling regimes. This behavior is rationalized through a phase diagram linking colloidal stability to optical response. These findings identify colloidal stability as a key parameter for designing soft plasmonic systems with programmable optical properties.
Published: 2026-04-17 09:05:22
Authors: Irem Ulku, Erdem Akagündüz, Ömer Özgür Tanrıöver
Categories: cs.CV, cs.AI
Abstract:
Multimodal remote sensing data provide complementary information for semantic segmentation, but in real-world deployments, some modalities may be unavailable due to sensor failures, acquisition issues, or challenging atmospheric conditions. Existing multimodal segmentation models typically address missing modalities by learning a shared representation across inputs. However, this approach can introduce a trade-off by compromising modality-specific complementary information and reducing performance when all modalities are available. In this paper, we tackle this limitation with CBC-SLP, a multimodal semantic segmentation model designed to preserve both modality-invariant and modality-specific information. Inspired by the theoretical results on modality alignment, which state that perfectly aligned multimodal representations can lead to sub-optimal performance in downstream prediction tasks, we propose a novel structured latent projection approach as an architectural inductive bias. Rather than enforcing this strategy through a loss term, we incorporate it directly into the architecture. In particular, to use the complementary information effectively while maintaining robustness under random modality dropout, we structure the latent representations into shared and modality-specific components and adaptively transfer them to the decoder according to the random modality availability mask. Extensive experiments on three multimodal remote sensing image sets demonstrate that CBC-SLP consistently outperforms state-of-the-art multimodal models across full and missing modality scenarios. Besides, we empirically demonstrate that the proposed strategy can recover the complementary information that may not be preserved in a shared representation. The code is available at https://github.com/iremulku/Multispectral-Semantic-Segmentation-via-Structured-Latent-Projection-CBC-SLP-.
Published: 2026-04-17 08:56:57
Authors: Tobias Abilock Mikkelsen, Cristian Placinta, Jesper Glückstad, Mirza Karamehmedović
Categories: physics.optics
Abstract:
We present a phase-only time-reversal framework for steering photonic nanojets without mechanical motion or amplitude modulation. Time-reversed radiation by a synthetic source placed at the target PNJ location helps define a phase-only modulation on a control line, compatible with a spatial light modulator, that produces the desired PNJ. Full-wave finite-difference frequency-domain (FDFD) simulations demonstrate robust lateral and axial steering with subwavelength confinement and low sidelobes. A parametric study of microelement geometries shows that nanojet formation is largely insensitive to moderate boundary variations, with simple shapes providing competitive performance. Robustness to fabrication and alignment errors is confirmed via uncertainty analysis.
Published: 2026-04-17 08:45:03
Authors: Dariusz Kosz, Jakub Niksiński, Błażej Wróbel
Categories: math.NT, math.CA, math.CO
Abstract:
We prove a uniform upper and lower bound for Delannoy numbers. This is achieved by using the representation of Delannoy numbers as the number of lattice points in high-dimensional cross-polytopes (also known as hyper-octahedrons or $\ell^1$ balls) and proving a uniform (dimension-free) count for these lattice points. Using this count, we establish dimension-free estimates for discrete maximal functions over cross-polytopes. By proving a comparison principle with the continuous setting, we obtain a dimension-free estimate on all $\ell^p(\mathbb{Z}^d)$ spaces for radii $R>C d^{3/2}.$ We also treat the full maximal function on $\ell^2(\mathbb{Z}^d)$ for small radii $R\le d^{1-\varepsilon}$ and the dyadic maximal function for any radii.
Published: 2026-04-17 08:44:55
Authors: Tomasz Kania
Categories: math.LO, math.FA, math.GR, math.KT, math.OA
Abstract:
We develop a unified framework for locating natural properties of algebraic and analytic structures within the Borel hierarchy. Objects are presented as quotients of a universal generator and definability is read directly from the quotient data.
For separable Banach-type structures (Banach algebras, $C^*$-algebras, Banach lattices, TROs) the kernel space is Polish under the Wijsman topology, and the quotient-norm functional $K\mapsto \|x+K\|$ is continuous, yielding a uniform definability scheme whose Borel ranks are bounded by quantifier alternation depth. For countable algebraic structures (groups, rings, lattices) we work on compact Polish spaces of congruences where atomic predicates are clopen.
We obtain explicit Borel upper bounds: in the \emph{unital} $C^*$-algebra coding based on $C^*_{\max}(F_\infty)$, stable finiteness is closed, nuclearity is Borel, simplicity is~$G_δ$, AF-ness lies in~$Π^0_3$, nuclear dimension~$\le n$ lies in~$Π^0_3$, and for fixed exact~$D$, $D$-absorption is analytic. For countable groups, soficity is~$G_δ$; for abelian groups, slenderness is~$Π^0_3$. We give an internal Borel coding of the $K_0$-assignment in the quotient/Wijsman framework; for each fixed coordinate the corresponding section is $F_σ$, and suspension together with Bott periodicity yields Borel codings of all higher $K$-groups. We also show that several bounds are optimal ($Σ^0_2$- and $Π^0_2$-complete). To calibrate the method's reach, we exhibit a $Π^1_1$-complete property (separable dual in the commutative $C^*$-setting), provably outside the Borel hierarchy.
Published: 2026-04-17 08:40:48
Authors: Chengwu Liu, Yichun Yin, Ye Yuan, Jiaxuan Xie, Botao Li, Siqi Li, Jianhao Shen, Yan Xu, Lifeng Shang, Ming Zhang
Categories: cs.AI, cs.CL, cs.LO
Abstract:
Most ATP benchmarks embed the final answer within the formal statement -- a convention we call "Easy Mode" -- a design that simplifies the task relative to what human competitors face and may lead to optimistic estimates of model capability. We call the stricter, more realistic setting "Hard Mode": the system must independently discover the answer before constructing a formal proof. To enable Hard Mode research, we make two contributions. First, we release MiniF2F-Hard and FIMO-Hard, expert-reannotated Hard Mode variants of two widely-used ATP benchmarks. Second, we introduce Discover And Prove (DAP), an agentic framework that uses LLM natural-language reasoning with explicit self-reflection to discover answers, then rewrites Hard Mode statements into Easy Mode ones for existing ATP provers. DAP sets the state of the art: on CombiBench it raises solved problems from 7 (previous SOTA, Pass@16) to 10; on PutnamBench it is the first system to formally prove 36 theorems in Hard Mode -- while simultaneously revealing that state-of-the-art LLMs exceed 80% answer accuracy on the same problems where formal provers manage under 10%, exposing a substantial gap that Hard Mode benchmarks are uniquely suited to measure.
Published: 2026-04-17 08:40:28
Authors: Zhaobo Hu, Vincent Gauthier, Mehdi Naima
Categories: cs.LG
Abstract:
Distribution shift severely degrades the performance of deep forecasting models. While this issue is well-studied for individual time series, it remains a significant challenge in the spatio-temporal domain. Effective solutions like instance normalization and its variants can mitigate temporal shifts by standardizing statistics. However, distribution shift on a graph is far more complex, involving not only the drift of individual node series but also heterogeneity across the spatial network where different nodes exhibit distinct statistical properties. To tackle this problem, we propose Reversible Residual Normalization (RRN), a novel framework that performs spatially-aware invertible transformations to address distribution shift in both spatial and temporal dimensions. Our approach integrates graph convolutional operations within invertible residual blocks, enabling adaptive normalization that respects the underlying graph structure while maintaining reversibility. By combining Center Normalization with spectral-constrained graph neural networks, our method captures and normalizes complex Spatio-Temporal relationships in a data-driven manner. The bidirectional nature of our framework allows models to learn in a normalized latent space and recover original distributional properties through inverse transformation, offering a robust and model-agnostic solution for forecasting on dynamic spatio-temporal systems.
Published: 2026-04-17 08:39:54
Authors: Zengyu Pang, Hua Xiang
Categories: quant-ph
Abstract:
Semi-quantum signature (SQS) schemes aim to enable quantum signature functionality in scenarios where only a subset of participants possess full quantum capabilities, thereby improving practical deployability while preserving quantum security advantages. Within this framework, we present a practical SQS protocol based on Bell states. The protocol is designed so that only the signer requires full quantum capability, significantly alleviating the quantum burden on the remaining participants. To strengthen security in semi-quantum environments, we incorporate an improved eavesdropping-detection mechanism that more effectively detects tampering. Compared with many existing schemes, which do not explicitly consider tampering of already generated signatures in their unforgeability analyses, the proposed protocol is designed to remain secure in the presence of such tampering.
Published: 2026-04-17 08:39:21
Authors: Yuheng Song, Wanzhu Chang, Kefeng Huang, Kaixin Sun, Chen Yao, Jianjun Ma
Categories: physics.app-ph
Abstract:
Rain introduces broadband and frequency-selective attenuation in wideband terahertz (THz) links, making it necessary to identify a compact spectral descriptor that captures how the dominant loss region evolves with rainfall conditions. This article investigates the peak-frequency behavior of rain attenuation by combining Mie-theory calculations with one separable laboratory Gaussian drop-size distribution (DSD) and seven outdoor empirical DSD models whose spectral shapes vary with rainfall rate. The analysis compares total-loss, absorption, and scattering components, examines the roles of characteristic DSD scale and representative drop-size statistics, and evaluates the effect of temperature on the peak location. The results show that, unlike the fixed-shape laboratory case where the peak frequency remains unchanged with rainfall rate, all outdoor empirical DSD models exhibit a monotonic migration of the attenuation peak toward lower frequencies as rainfall rate increases; this behavior is well described by an asymptotic power-law relation and is governed primarily by the rainfall-dependent DSD characteristic scale rather than by total drop concentration or fixed-temperature dielectric dispersion.
Published: 2026-04-17 08:35:12
Authors: Ashleigh, Ratcliffe, Tho, Nguyen Xuan
Categories: math.GM
Abstract:
In Section 6.6 of the book {\it Number Theory, Volume I: Tools and Diophantine Equations, Graduate Texts in Mathematics, Volume 239, Springer (2007)}, Cohen investigated the solubility of the equation $n=x^4+y^4$ in the rational numbers $x,y$ for all positive integers $n \leq 10000$. Motivated by this, we investigate the equation $n=x^4-y^4$ and obtain the complete list of positive integers $n\leq 10000$ that can be represented in this form for some nonzero rational numbers $x$ and $y$.
Published: 2026-04-17 08:32:39
Authors: Jun Li, Lizhi Xiong, Ziqiang Li, Weiwei Jiang, Zhangjie Fu, Yong Li, Guo-Sen Xie
Categories: cs.CV, cs.CR
Abstract:
Text-to-image generative models have achieved impressive fidelity and diversity, but can inadvertently produce unsafe or undesirable content due to implicit biases embedded in large-scale training datasets. Existing concept erasure methods, whether text-only or image-assisted, face trade-offs: textual approaches often fail to fully suppress concepts, while naive image-guided methods risk over-erasing unrelated content. We propose TICoE, a text-image Collaborative Erasing framework that achieves precise and faithful concept removal through a continuous convex concept manifold and hierarchical visual representation learning. TICoE precisely removes target concepts while preserving unrelated semantic and visual content. To objectively assess the quality of erasure, we further introduce a fidelity-oriented evaluation strategy that measures post-erasure usability. Experiments on multiple benchmarks show that TICoE surpasses prior methods in concept removal precision and content fidelity, enabling safer, more controllable text-to-image generation. Our code is available at https://github.com/OpenAscent-L/TICoE.git