Published: 2026-05-15 15:54:29
Authors: Chiara Sorgentone, Enza Pellegrino, Francesca Pitolli
Categories: math.NA
Abstract:
Fractional boundary value problems are often used to model complex systems and processes characterized by memory effects and anomalous diffusion. In this paper, we consider fractional boundary value problems involving the Riesz-Caputo operator, which is particularly suited for modeling physical phenomena exhibiting symmetric diffusive effects. We provide an integral representation of the solution to prove existence and uniqueness of the fractional differential problem. We introduce a B-spline collocation method to approximate the solution of the problem and provide a convergence analysis, with both theoretical insights and numerical experiments.
Published: 2026-05-15 15:54:08
Authors: Richard Healey
Categories: quant-ph, physics.hist-ph
Abstract:
Measurement is an important scientific activity. In most of science, including classical physics, is may be understood as a way of finding out about the physical world and representing the results numerically. No-go theorems show that measurement of quantum observables is not like that: the recorded outcome is typically created rather than revealed in a quantum measurement, in which case there is no objective fact about the observable's prior value. Other no-go theorems show that unitary quantum theory can generally neither explain nor even represent a unique recorded outcome, thereby threatening that outcome's objectivity. Methodological norms inherent in quantum physical practice nevertheless institute the objectivity, not only of unique recorded outcomes of quantum measurements, but also of non-quantum features of the world that physicists and other scientists take their models to represent.
Published: 2026-05-15 15:54:01
Authors: R. M. Potvliege
Categories: physics.atom-ph
Abstract:
We study the role of retardation in the energy shift of Rydberg states induced by thermal radiation, focusing on the case of temperatures higher than those for which the electric-dipole approximation is expected to apply. As anticipated by Farley and Wing [Phys. Rev. A {\bf 23}, 2397 (1981)], retardation needs to be taken into account in calculations of this energy shift at and above the temperature $α\, mc^2/(3k_{\rm B}\,n^2)$, where $n$ is the principal quantum number of the state considered, $m$ is the mass of the electron and $k_{\rm B}$ is Boltzmann constant.The corresponding non-dipole shift dominates the electric-dipole shift at about 2.5 times that characteristic temperature. We also show that the electric-quadrupole thermal shift is of the same order of magnitude as the diamagnetic thermal shift and would thus need to be taken into account in the circumstances where the latter is relevant.
Published: 2026-05-15 15:53:34
Authors: Wei Sun, Yijun Chen, Bo Gao, Ke Xiong, Yuwei Wang, Pingyi Fan, Khaled Ben Letaief
Categories: cs.CR, cs.DC
Abstract:
Federated learning (FL) is vulnerable to data poisoning attacks due to its distributed nature. Although recent GAN-based data poisoning methods have indicated the potential of using generative AI to generate seemingly legitimate poisoned data, the inherent consistency of GAN outputs can still reveal a sign of data poisoning. In this paper, we propose a diffusion-based data poisoning framework against FL systems, which leverages a Poisoning-Oriented Conditional Diffusion Model (PCDM) to enable fine-grained control over the local generation of poisoned data while ensuring both attack effectiveness and stealthiness. Our PCDM incorporates an adjustable poisoning vector within the global context to precisely control the generation of poisoned data, with theoretical guarantees on attack performance. Furthermore, it employs a novel jumping diffusion strategy for lightweight and efficient poisoned data generation. We conduct the most systematic and broad experimental evaluation for FL poisoning attacks against various defenses, including advanced Byzantine robust aggregation mechanisms, on four open datasets: MNIST, Fashion-MNIST, CIFAR-10, CIFAR-100, and a real-world wireless-specific dataset VRAI. Our results demonstrate that PCDM is less likely to exhibit statistical anomalies compared with the state-of-the-art methods while more effectively degrading global FL performance, which poses a significant risk to data security in FL.
Published: 2026-05-15 15:51:21
Authors: Michael Megrelishvili
Categories: math.GN, math.DS, math.FA
Abstract:
We introduce the median uniformity $\mathcal U_{\mathrm m}$, an intrinsic precompact convex uniform structure on a median algebra. It is Hausdorff under natural assumptions, for instance for finite-rank median algebras. In the Hausdorff case, its uniform completion yields the Minimal Median Compactification (MMC). The induced topology $τ_{\mathrm m}$ provides a natural higher-rank analogue of the interval topology on linearly ordered sets and of the shadow topology on rank-one median algebras. When all intervals in the median algebra $X$ are finite, the MMC is the unique proper median compactification of $(X,τ_{\mathrm m})$; in particular, it coincides with the Roller compactification. We apply this uniform framework to continuous actions of a topological group $G$ by median automorphisms. We show that the MMC is a median $G$-compactification. In the finite-rank case, the resulting compact $G$-system is Rosenthal representable and hence dynamically tame.
Published: 2026-05-15 15:47:41
Authors: Hao Yang, Zhuo Ma, Yang Liu, Yilong Yang, Guancheng Wang, JianFeng Ma
Categories: cs.CR, cs.CV
Abstract:
Large vision-language models (LVLMs) have emerged as a powerful paradigm for multimodal intelligence, but their growing deployment also expands the attack surface of prompt injection. Despite this growing concern, existing attacks still suffer from a critical limitation: the injected prompt for one modality only steers the model's interpretation of that singular input. Alternatively, these attacks remain multimodal but fail to achieve cross-modal prompt perturbation. To bridge this gap, we introduce a novel cross-modal prompt injection attack CrossMPI, which can steer the model's interpretation of both textual and visual inputs via image-only prompt injection. Our design is underpinned by the following key breakthroughs. First, we turn the focus of the injected prompt perturbation optimization from the visual embedding space (typically with only $10^5$ parameters) to the model hidden state space (for multimodal information integration and with $10^7$ parameters). Then, two strategies are adopted to mitigate the optimization challenges posed by the larger parameter space. To constrain the optimized model parameter space, we introduce a layer selection strategy that identifies the layers most critical to multimodal integration. Interestingly, deviating from the past experience, our analysis reveals that the optimal layers for LVLM prompt perturbation reside in the middle of the model rather than the last. To constrain the image perturbation space, we propose a new distance-decremental perturbation budget assignment strategy that allocates budgets decrementally as the pixel distance to semantic-critical regions increases. Extensive experiments across multiple LVLMs and datasets show that our method significantly outperforms baseline approaches.
Published: 2026-05-15 15:47:01
Authors: Till Beemelmanns, Shayan Sharifi, Manas Mehrotra, Ayushman Choudhuri, Lutz Eckstein
Categories: cs.RO, cs.AI
Abstract:
Deep Neural Networks have become the dominant solution for Autonomous Driving perception, but their opacity conflicts with emerging Trustworthy AI guidelines and complicates safety assurance, debugging, and human oversight. While theoretical frameworks for safe and Explainable AI (XAI) exist, concrete implementations of Trustworthy AI for 3D scene understanding remain scarce. We address this gap by proposing a Trustworthy AI perception module that is remarkably robust, integrates faithful explainability, and calibrated uncertainty estimates. Building on a transformer-based detector, we derive explanation from the attention mechanism at inference time and validate their faithfulness using perturbation-based consistency tests. We further integrate an uncertainty estimation and calibration module, and apply robustness-enhancing training methods. Experiments show faithful saliency behavior, improved robustness, and well-calibrated uncertainty estimates. Finally, we deploy these Trustworthy AI elements in a prototype vehicle and provide an XAI Interface that visualizes documentation artifacts, model uncertainty state, and saliency maps, demonstrating the feasibility of trustworthy perception monitoring in real time. Supplementary materials are available at https://tillbeemelmanns.github.io/trustworthy_ai/ .
Published: 2026-05-15 15:46:45
Authors: Xinyi Li, Yushu Zheng
Categories: math.PR
Abstract:
We study downward deviations of the maximum local time of the discrete-time simple random walk on $\mathbb{Z}^d$, $d\ge 3$. In our previous paper \cite{li2026ldmaxlocal}, the corresponding upper bound was established, while the matching lower bound was left open. In the present paper, we prove this lower bound and hence obtain the sharp asymptotic formula for the downward-deviation probability. To provide a discrete-time analogue of the jump-chain/holding-time structure used in the continuous-time argument, we introduce a new random structure which we name as {\it loop-pruned random walk} and the associated loop-pruning decomposition, which is also of independent interest.
Published: 2026-05-15 15:34:48
Authors: Markus Riedle
Categories: math.PR
Abstract:
We develop a stochastic integration theory for predictable integrands with respect to a Lévy basis. Our approach is based on decoupling inequalities for tangent sequences and reduces the construction of the stochastic integral essentially to the deterministic integration theory for infinitely divisible random measures developed by Rajput and Rosiński.
We characterise the corresponding class of integrable predictable processes in terms of the semimartingale characteristics associated with the driving random measure and show that the resulting space of integrands possesses a natural Musielak-Orlicz type structure equipped with an F-norm. Furthermore, we establish continuity properties of the integral operator and a stochastic version of Lebesgue's dominated convergence theorem.
Published: 2026-05-15 15:32:54
Authors: Alberto Ocana, Jorge R. Espinosa
Categories: cond-mat.soft, physics.bio-ph
Abstract:
Antibody-based therapeutics-including antibody-drug conjugates (ADCs), bispecific antibodies, and novel formats-are reshaping oncology, yet key determinants of efficacy, safety, and manufacturability frequently emerge after conjugation and formulation. We argue that computational biophysics provides an underexploited framework to address this gap by connecting molecular interactions to biological outcomes. We highlight how molecular dynamics, coarse-grained simulations, and free energy calculations reveal how conjugation site, linker chemistry, and drug-antibody ratio reshape conformational landscapes. We emphasize structural coupling between antibody, linker, and payload, with implications for antigen binding, internalization, and developability. We propose that integrating physics-based modeling into development pipelines-alongside experimental validation-can reduce empirical iteration and de-risk translation. As force fields, and hybrid physics-machine-learning methods improve, this field is poised to become a central driver of next-generation ADC design.
Published: 2026-05-15 15:29:30
Authors: Raushan Joshi, Jean-Yves Guillemaut
Categories: cs.CV, cs.AI
Abstract:
3D Gaussian Splatting (3D-GS) enables real-time 3D scene reconstruction but lacks robust segmentation for editing tasks such as object removal, extraction, and recoloring. Existing approaches that lift 2D segmentations to the 3D domain suffer from view inconsistencies and coarse masks. In this paper, we propose a novel framework that leverages the Segment Anything Model High Quality (SAM-HQ) to generate accurate 2D masks, addressing the limitations of the standard SAM in boundary fidelity and fine-structure preservation. To achieve robust 3D segmentation of any target object in a given scene, we introduce a prior-guided label reassignment method that assigns labels to 3D Gaussians by enforcing multiview consistency with learned priors. Our approach achieves state-of-the-art segmentation accuracy and enables interactive, real-time object editing while maintaining high visual fidelity. Qualitative results demonstrate superior boundary preservation and practical utility in Virtual Reality (VR) and robotics, advancing 3D scene editing.
Published: 2026-05-15 15:21:25
Authors: Fan Feng, Selena Ge, Minghao Fu, Zijian Li, Yujia Zheng, Zeyu Tang, Yingyao Hu, Biwei Huang, Kun Zhang
Categories: cs.LG, cs.AI
Abstract:
Recent work has framed decision-making as a sequence modeling problem using generative models such as diffusion models. Although promising, these approaches often overlook latent factors that exhibit evolving dynamics, elements that are fundamental to environment transitions, reward structures, and high-level agent behavior. Explicitly modeling these hidden processes is essential for both precise dynamics modeling and effective decision-making. In this paper, we propose a unified framework that explicitly incorporates latent dynamic inference into generative decision-making from minimal yet sufficient observations. We theoretically show that under mild conditions, the latent process can be identified from small temporal blocks of observations. Building on this insight, we introduce Ada-Diffuser, a causal diffusion model that learns the temporal structure of observed interactions and the underlying latent dynamics simultaneously, and furthermore, leverages them for planning and control. With a modular design, Ada-Diffuser supports both planning and policy learning tasks, enabling adaptation to latent variations in dynamics, rewards, and latent actions. Experiments on simulated control and robotic benchmarks demonstrate its effectiveness in accurate latent inference and adaptive policy learning.
Published: 2026-05-15 15:13:42
Authors: S. Huyan, R. F. S. Penacchio, D. Zhang, Z. Li, S. L. Morelhão, Raquel Ribeiro, P. C. Canfield, S. L. Bud'ko
Categories: cond-mat.mtrl-sci, physics.app-ph
Abstract:
The creation and exploration of new materials under extreme pressure-temperature conditions has become increasingly reliant on laser-heated diamond anvil cell (LHDAC) techniques, which provide direct access to previously unexplored regions of multinary phase diagrams. Whereas numerous high-pressure phases have been identified in situ, systematic recovery and post-synthesis physical property characterization of these materials remain significant challenges. In this work, we present the development of an integrated LHDAC synthesis and demonstrate a practical LHDAC-based synthesis workflow that enables stabilization and recovery of metastable intermetallic phases for subsequent structural and transport studies. Using this approach, we successfully achieved LHDAC synthesis of high-pressure MnSb2 and YbZn2 phases under moderate pressures. Synchrotron X-ray diffraction and spatial mapping confirm dominant formation of the targeted phases, whereas laboratory-based refinement quantifies phase fractions despite intrinsic microstrain and minor secondary phases. High-pressure transport measurements on recovered samples reveal tunable by pressure electronic instabilities in both systems. In MnSb2, pressure suppresses two high-temperature magnetic ordering anomalies, observed in transport, by 5 GPa and for higher pressures induces a new low-temperature feature that increases with further pressure increase. In hexagonal high-pressure YbZn2, an electronic reconstruction emerges at ~11 GPa, characterized by semiconducting-like behavior from ~ 30 K to 300 K and a broad low-temperature coherence crossover near 30 K. Our results establish LHDAC synthesis not only as a structural discovery tool, but also as an experimental platform for investigating correlated quantum states stabilized far from equilibrium thermodynamic conditions.
Published: 2026-05-15 15:08:05
Authors: Dietmar Ferger
Categories: math.ST
Abstract:
We consider the problem of testing the mean of high-dimensional data when the dimension may grow without explicit rate restrictions relative to the sample size. The proposed procedure is based on the statistic V_n = n||Xn||^2, which avoids inversion of the covariance matrix and is therefore suitable for high-dimensional settings.We establish asymptotic distributional results for both fixed and increasing dimension by embedding the observations into the Hilbert space l2. Furthermore, we prove the asymptotic validity of a bootstrap approximation for the distribution of the test statistic. The resulting bootstrap test yields asymptotic level-a procedures without requiring sparsity assumptions or structural conditions on the covariance matrix. In all this, a new Central Limit Theorem in l2 is proving to be an extremely useful tool.
Published: 2026-05-15 15:06:06
Authors: Zhiwei Yang, Mengyu Li, Xiaohang Xie, Ao Chen, Thomas G. Bifano, Xin Zhang
Categories: physics.app-ph
Abstract:
Ventilated acoustic silencers combing sound attenuation with high ventilation are pivotal for advanced noise control. However, balancing attenuation, bandwidth, openness, and thickness remains a high-dimensional challenge. Here, we report a physics-aware machine-learning-driven inverse design framework for ultra-open acoustic silencers (UAS). By leveraging Green's function-based parameterization, we physically decouple the design space into spectral and radial parameters, ensuring physical interpretability while reducing complexity. We introduce a two-stage forward prediction architecture that captures broadband envelopes and sharp resonant features via a coarse-to-fine strategy. Coupled with a population-based, hybrid-objective parallel (PHP) inverse strategy, our framework enables rapid exploration of non-convex landscapes, identifying hundreds of optimized candidates within seconds. Crucially, this framework uncovers hidden linear design rules that govern high-performance monolithic designs, acting as geometric proxies for optimal impedance-matching. We experimentally validate a family of prototypes: UAS-2 demonstrates the monolithic limit with high ventilation ratio, while UAS-3 demonstrates versatility in multi-mode interactions. To circumvent the trade-off ceiling of single-unit resonators, a parallel-composite architecture (UAS-4) is introduced to enhance performance through spatial interference distribution. Results confirm a broadband bandwidth exceeding 830 Hz achieved with an ultra-thin profile (0.1-0.2λ) and 80% ventilation. This work establishes a data-driven paradigm for discovering design principles in functional metamaterials.
Published: 2026-05-15 15:04:31
Authors: Oliver Bernardi, Jonathan J. Fang
Categories: math.CO
Abstract:
A classical enumerative result states that, given a graph $G$ and a vertex $u$, the number of connected subgraphs of $G$ is equal to the number of orientations of $G$ such that every vertex can reach $u$ by a directed path. We show that this result is an instance of a much broader set of enumerative identities between subgraphs and orientations corresponding to various connectivity constraints. Namely, given two sets of pairs of vertices $A=\{(u_i,v_i), i\in[k]\}$ and $B=\{(u_i',v_i'), i\in[l]\}$, we consider the orientations $α$ of $G$ such that adding the elements of $A$ and $B$ as additional directed edges to $α$ gives an orientation $α'$ in which $v_i$ cannot reach $u_i$ for all $i\in[k]$, but $v_i'$ can reach $u_i'$ for all $i\in[l]$. We show that this set of orientations is equinumerous to a set of subgraphs satisfying the ``same" connectivity constraints defined in terms of $A$ and $B$.
We also extend our results to the enumeration of equivalence classes of orientations satisfying such connectivity constraints. Precisely, we consider the equivalence classes under cycle reversal, cocycle reversal, or cycle-cocycle reversal. We show that the equivalences classes are equinumerous to some sets of subgraphs defined by connectivity and acyclicity constraints.
Published: 2026-05-15 14:57:21
Authors: Nils Feldhus, Tanja Baeumel, Elena Golimblevskaia, Qianli Wang, Van Bach Nguyen, Aaron Louis Eidt, Christopher Ebert, Wojciech Samek, Jing Yang, Vera Schmitt, Sebastian Möller, Simon Ostermann
Categories: cs.CL, cs.LG
Abstract:
LLM-as-a-judge has become the dominant paradigm for grading model outputs at scale, yet the same model assigns systematically different scores when its output format changes (e.g., a 1-5 rating vs. a True/False label). Existing diagnoses of these format-induced inconsistencies stop at the input-output level. Using Position-aware Edge Attribution Patching (PEAP), we causally investigate the internal mechanism in Gemma-3, Qwen2.5, and Llama-3. We find that judgments across structured understanding and open-ended preference tasks share a sparse, generalized Latent Evaluator sub-graph in the mid-to-late multi-layer perceptrons (MLPs); zero-ablating it collapses judgment while preserving world knowledge in architecturally modular models. By structurally decoupling abstract judging from output formatting, we provide a mechanistic account of format-induced inconsistency on the open-weight models we study: a continuous judgment signal computed in the shared trunk is mapped through fragile, format-specific terminal branches, enabling format-independent preference to be isolated downstream of the requested output format. Our findings imply that benchmark-level reliability comparisons across formats are partially measuring formatter geometry rather than evaluation quality.
Published: 2026-05-15 14:40:19
Authors: Scott Fredriksson, Akshit Saradagi, George Nikolakopoulos
Categories: cs.RO
Abstract:
Local navigation is one of the fundamental problems in robot navigation, and numerous approaches have been proposed over the years, including methods such as the Dynamic Window Approach, Model Predictive Control, and more recently, Control Barrier Functions and machine learning based techniques. While these methods perform well in simple environments, many of them rely on optimization or learning based procedures that can struggle in more complex scenarios. In contrast, this article proposes a more geometric algorithmic approach that enables a local navigation method with faster computation times and longer planning horizons. The proposed method is based on the computation of a sequence of circular regions from a local LiDAR scan that expand in the direction of the goal and capture free local navigable space. The proposed method was implemented in the ROS2 framework and evaluated in a simulated environment.
Published: 2026-05-15 14:35:33
Authors: Thomas Ransford
Categories: math.FA, math.CV
Abstract:
We establish the following result, confirming a conjecture of Jean Esterle. For each closed subset $E$ of the unit circle of Lebesgue measure zero, there exists a positive sequence $u_n\to\infty$ with the following property: if $T$ is a contraction on a Hilbert space such that $σ(T)\subset E$ and $\|T^{-n}\|=O(u_n)$ as $n\to\infty$, then $T$ is a unitary operator.
A key tool used in the proof is a result generalizing the well-known fact that closed subsets $E$ of the real axis of Lebesgue measure zero are removable for bounded holomorphic functions. We show that such sets remain removable even for certain unbounded holomorphic functions of moderate growth near $E$, where the notion of `moderate' depends on $E$.
Published: 2026-05-15 14:33:09
Authors: Mingqiang Wu, Weilun Feng, Zhefeng Zhang, Haotong Qin, Yuqi Li, Guoxin Fan, Xiaokun Liu, Zhulin An, Libo Huang, Yongjun Xu, Chuanguang Yang
Categories: cs.CV
Abstract:
Autoregressive video diffusion models enable open-ended generation through local attention and KV caching. However, existing training-free long-video optimization methods mainly focus on stable extension under a single prompt, making them difficult to handle interactive scenarios involving prompt switching, old scene forgetting, and historical scene recall. We identify the core bottleneck as the functional entanglement of historical KV states: stable anchors and recent dynamics are handled by the same cache policy, leading to outdated background contamination, delayed response to new prompts, and loss of long-range memory. To address this issue, we propose Echo-Forcing, a training-free scene memory framework specifically designed for interactive long video generation with three core mechanisms: (1) Hierarchical Temporal Memory, which decouples stable anchors, compressed history, and recent windows under relative RoPE; (2) Scene Recall Frames, which compresses historical scenes into spatially structured KV representations to support long-term recall; and (3) Difference-aware Memory Decay, which adaptively forgets conflicting tokens according to the discrepancy between old and new scenes. Based on these designs, Echo-Forcing uniformly supports smooth transitions, hard cuts, and long-range scene recall under a bounded cache budget. Extensive evaluations on VBench-Long further demonstrate that Echo-Forcing achieves the best overall performance in both long-video generation and interactive video generation settings. Our code is released in https://github.com/mingqiangWu/Echo-Forcing
Published: 2026-05-15 14:31:45
Authors: Gowrika Mahesh, Budanur Madappa Darshan Gowda, Kavana Gopladevarahalli Papegowda, Prajwal Basavaraj, Binh Vu, Swati Chandna, Mehrdad Jalali
Categories: cs.SI, cs.AI, cs.DL
Abstract:
Editors and reviewers are expected to ensure that manuscripts cite relevant, accurate, current, and ethically appropriate literature, yet manuscript-level citation auditing remains largely manual, fragmented, and difficult to scale. Citation context, metadata quality, self-citation patterns, and bibliographic integrity all affect whether a reference appropriately supports a local claim. We present CitePrism, a transparent hybrid decision-support framework for editorial citation auditing that combines LLM-assisted contextual reasoning, embedding-based semantic similarity, metadata verification, integrity-oriented flags, and human-in-the-loop analyst review. CitePrism extracts citation neighborhoods, enriches reference metadata, computes fused relevance scores, surfaces metadata and self-citation review prompts, and supports configurable threshold-based triage. In a preliminary validation on a single case-study manuscript with 104 references from pavement engineering, agreement with human binary relevance labels reached Cohen's kappa = 0.429. At operating threshold tau = 17, CitePrism flagged all human-labeled irrelevant citations, while also producing false positives requiring analyst review. These results suggest that CitePrism may support conservative editorial screening and citation-quality triage, but they do not establish general editorial performance. CitePrism is intended as pilot-stage decision support, not as an autonomous misconduct detector or automated editorial decision system. Broader validation across manuscripts, domains, annotators, baselines, and deployment settings is required before operational use.
Published: 2026-05-15 14:25:36
Authors: Felipe Taha Sant'Ana
Categories: cond-mat.quant-gas, cond-mat.stat-mech
Abstract:
We derive the canonical-ensemble scaling of Tan's contact for $N$ harmonically trapped Tonks--Girardeau bosons at finite temperature in the large-$N$ limit. The leading scaling coefficient reproduces the local-density-approximation result and is obtained from a contour-integral representation of the canonical partition function followed by a saddle-point reduction to a phase-space integral with a self-consistent scaled chemical potential. The subleading coefficient is the central new object of this work: it admits an explicit representation in terms of universal phase-space integrals of the Fermi factor, has closed-form Sommerfeld and virial limits, and is identified with the canonical-versus-grand-canonical ensemble difference at fixed mean particle number. In the high-temperature Boltzmann regime the ratio of subleading to leading coefficients collapses to a universal value, traceable to the Poissonian particle-number statistics of the dilute grand-canonical gas. We construct Padé approximants for both scaling functions that interpolate uniformly between the low-temperature Sommerfeld and high-temperature virial regimes; for the subleading coefficient we report a form that is uniformly accurate on our working range of temperatures and asymptotically correct beyond. The scaling law is verified against canonical contour-integration data across the full temperature range.
Published: 2026-05-15 14:24:27
Authors: Mordecki Ernesto, Muler Nora, Oliú Facundo
Categories: math.OC
Abstract:
We study a discounted singular stochastic control problem driven by a general Lévy process, where the objective is to minimize a cost functional composed of a running cost and a control cost that depends on the current state of the process. We first establish a Hamilton Jacobi Bellman (HJB)-type verification theorem providing sufficient conditions under which a reflecting barrier strategy is optimal and characterizing the value function. Our main contribution is to connect this control problem with an associated optimal stopping problem: we prove that the optimal reflection threshold coincides with the optimal stopping boundary of the auxiliary problem. This connection allows us to characterize the optimal strategy through probabilistic tools and leads to explicit or semi-explicit solutions in several relevant cases. We illustrate the results with several examples, including an application to pollution abatement.
Published: 2026-05-15 14:21:52
Authors: Isar Nejadgholi, Masoud Kianpour, Krishnapriya Vishnubhotla, Maryam Molamohamadi
Categories: cs.CL
Abstract:
Tremendous efforts have been put into evaluating the inclusivity and effectiveness of AI systems across cultures. However, the cultural capabilities considered in much of the literature remain vaguely defined, are referred to using interchangeable terminology, and are typically limited to recalling accurate information about various demographics, regions, and nationalities. To address this construct ambiguity, we draw from Intercultural Communication scholarship and propose a three-level taxonomy of AI-relevant cultural capabilities: Cultural Awareness answers "Does the model know?", Cultural Sensitivity answers "How does it frame its knowledge?", and Cultural Competence answers "Can it adapt as the interaction evolves?". Beyond conceptual clarification, we position this taxonomy as a practical tool for improving the validity and interpretability of AI evaluation in real-world, multicultural settings. Without such construct clarity, evaluation results risk overstating model capabilities and may lead to inappropriate deployment decisions in culturally sensitive contexts.
Published: 2026-05-15 14:19:37
Authors: Ömer Faruk Çoban, Diego F. Torres, Jian Li, Daniela Hadasch, Agnibha De Sarkar, Matthew Kerr
Categories: astro-ph.HE
Abstract:
SS 433 is a microquasar whose relativistic jets precess every ~162 days, providing a laboratory for jet-interstellar medium interactions. We present a comprehensive analysis of 16 years of Fermi Large Area Telescope data (August 2008-September 2024) of the SS 433/W50 field, using events in the 0.3-300 GeV range and employing pulsar gating to mitigate contamination from the bright nearby pulsar PSR J1907+0602. We detect the GeV source 4FGL J1913.2+0512 (TS = 45, where TS denotes the likelihood-ratio Test Statistic) with a power-law spectrum (photon index 2.61 +- 0.08) and confirm a GeV excess at the western lobe (TS = 17). The eastern lobe of SS 433 is hinted at with lower significance. One additional GeV excess, Fermi J1909.6+0552 (TS = 20; TS = 28 over 0.1-300 GeV), located outside the SS 433 / W50 system, is revealed after gating. Exposure-corrected Lomb-Scargle periodograms and precessional phase-folded light curves show a ~162-day modulation in 4FGL J1913.2+0512. This periodicity is prominent during the first 10 years of the mission (2008-2018) but disappears thereafter, with the phase-folded flux concentrated in precessional phases 0.0-0.5. Over the full 16-year dataset, the modulation remains detectable but with reduced significance, consistent with dilution by the later non-modulated epoch. These results indicate that the efficiency and/or geometry of gamma-ray production in the SS 433 environment evolves on multi-year timescales.
Published: 2026-05-15 14:15:50
Authors: Ido Lublin, Dor Atzmon, Izack Cohen
Categories: cs.AI
Abstract:
We formulate the Resource-Constrained Project Scheduling Problem (RCPSP) as optimal search over the reachability graph of a Timed Transition Petri Net with Resources, using relative-delay tokens so that scheduling decisions correspond to transition firings in the induced state space. We solve the resulting problem with $A^*$ guided by a heuristic that combines Critical Path and resource-based lower bounds, and prove that it is consistent under our token-based time semantics. Experiments on the PSPLIB benchmarks show that the approach outperforms strong exact Mixed-Integer Linear Programming (MIP) baselines (SCIP, CBC) in both success rate and solve time. Per-instance analysis shows that heuristic search and MIP degrade along independent axes, resource tightness for $A^*$ and formulation size for MIP, with resource strength mediating which solver benefits from scale.
Published: 2026-05-15 14:15:13
Authors: Haoran Gu, Yubo Zhao, Siyuan Cheng, Yuee Xie, Xiaosen Yang, Yuanping Chen
Categories: quant-ph
Abstract:
Dynamical quantum phase transitions in non-Hermitian systems pose fundamental challenges due to the intrinsic biorthogonality of their eigenstates. In this work, we extend a biorthogonal framework to investigate dynamical quantum phase transitions in non-Hermitian topological superconductors. Taking the non-Hermitian Kitaev chain as a prototypical model, we construct an associated-state formalism and reformulate the Loschmidt rate function, dynamical topological order parameter, and dynamical Fisher zeros. Within this framework, we find that the critical times at which dynamical quantum phase transitions occur differ from those based on the conventional self-normal approaches. We further analyze momentum-resolved subsystems at critical momenta and demonstrate the robustness of the biorthogonal framework. Our work highlights the essential role of biorthogonality in nonequilibrium dynamics and establishes a consistent theoretical framework for dynamical quantum phase transitions in non-Hermitian topological superconductors.
Published: 2026-05-15 14:11:23
Authors: Ernesto Garcia-Estrada, Carlos Escolano, José A. R. Fonallosa
Categories: cs.CL, cs.AI
Abstract:
Production machine translation relies overwhelmingly on encoder-decoder Seq2Seq models, yet reinforcement learning approaches to MT fine-tuning have largely targeted decoder-only LLMs at $\geq$7B parameters, with limited systematic study of encoder-decoder architectures. We apply Group Relative Policy Optimization to NLLB-200 (600M and 1.3B) using a hybrid reference-free reward (LaBSE and COMET-Kiwi) that requires no parallel data at fine-tuning time, evaluating across 13 typologically diverse languages. GRPO yields consistent improvements on all 13 languages, up to $+$5.03 chrF++ for Traditional Chinese, and, without any target-language data, competes with 3-epoch supervised fine-tuning on morphologically complex languages . We identify a consistent empirical pattern in which gains are largest where baseline performance is weakest and reward discriminability is highest, making this approach most effective precisely where parallel data is scarcest, and replicate this pattern across English and Spanish source languages.
Published: 2026-05-15 14:05:01
Authors: G. Shankar, Joseph Maciejko
Categories: cond-mat.str-el, cond-mat.mes-hall, quant-ph
Abstract:
The Lieb-Schultz-Mattis (LSM) theorem and its higher-dimensional extensions forbid the existence of a unique, symmetric, and gapped ground state at fractional fillings in quantum many-body systems with a conserved particle number (or spin angular momentum) and the conventional translation symmetry of Euclidean lattices. In this work, we propose a generalization of the LSM theorem to quantum many-body systems on hyperbolic lattices, i.e., regular tessellations of two-dimensional negatively curved space. By leveraging concepts from hyperbolic band theory in a many-body setting, we adapt Oshikawa's flux-threading argument to periodic hyperbolic lattices with a non-Euclidean (Fuchsian) translation symmetry and compute a lower-bound to the ground-state degeneracy as a function of filling and lattice geometry. We explore the consequences of LSM constraints for gapped phases of hyperbolic quantum matter and suggest frustrated spin models on hyperbolic analogs of the square and triangular lattices as promising platforms for realizing symmetric spin liquids in hyperbolic space.
Published: 2026-05-15 13:05:11
Authors: Jaime Paradela
Categories: math.DS
Abstract:
We consider the direct product of two symplectomorphisms, one of which exhibits a basic set and the other one a non-degenerate elliptic equilibrium. Under a domination condition we show that a broad class of real-analytic deformations of this system display large robustly transitive sets. As a corollary of our construction we also obtain new examples of real-analytic robustly transitive symplectomorphisms which are not uniformly hyperbolic.
To establish these results we develop perturbation techniques to create blender horseshoes in the real-analytic setting and import ideas from control theory which show that, typically, these objects have a large domain of influence.
Published: 2026-05-15 12:54:56
Authors: An Nguyen, Jaesik Choi, Anh Tong
Categories: cs.LG, cs.AI, cs.CV
Abstract:
Parameter-efficient fine-tuning (PEFT) has emerged as an critical technique for adapting large-scale foundation models across natural language processing and computer vision. While existing methods such as low-rank adaptations achieve parameter efficiency via low-rank weight updates, they are limited in their ability to preserve the geometric structure of pretrained representations. We introduce Low-rank Compositional Orthogonal fine-tuning (LoCO), a novel PEFT method that constructs orthogonal transformations through low-rank skew-symmetric matrices and compositional rotation chains. We propose an approximation scheme that enables fully parallel computation of compositional rotations, making the approach practical for high-dimensional feature spaces. Our method maintains low computational complexity while maintaining orthogonality with controlled approximation error. We validate LoCO across diverse domains, including diffusion transformer fine-tuning, vision transformer adaptation, and language model adaptation. Our method demonstrates superior or competitive performance compared to both existing orthogonal and non-orthogonal methods.
Published: 2026-05-15 12:49:18
Authors: Shuya Zhou, Junwen Xia, Jingxiao Zhang
Categories: stat.ME
Abstract:
High-dimensional classification problems often rely on the Lasso-penalized linear Support Vector Machines (SVMs). However, the double non-smoothness induced by the hinge loss and Lasso penalty in this model makes statistical inference challenging and impedes computational efficiency. In this paper, we propose a unified inference framework in both offline and online settings. In the offline case, by applying a convolution smoothing technique to the hinge loss, we construct a debiased estimator that eliminates the shrinkage bias, thereby building a valid confidence interval. For online streaming data, we develop a real-time estimator and inference procedure that relies only on summary statistics of historical data. Theoretically, we provide rigorous proofs for the asymptotic normality of our offline and online debiased estimators. Simulation studies and real data applications demonstrate that our methods achieve valid statistical inference and improved computational efficiency.
Published: 2026-05-15 12:27:42
Authors: Robin Van Oirbeek, Tim Verdonck
Categories: stat.ME, stat.AP
Abstract:
The correct inferential object in claims reserving is the conditional predictive distribution $p(R \mid \mathcal{D}, \hatθ)$, where $\mathcal{D}$ is the observed triangle held fixed. We refer to this as the conditioning principle. All existing bootstraps violate it by resampling functions of $\mathcal{D}$ inside the predictive loop, producing an $O(1)$ coverage error that does not vanish as the triangle grows.
The Dirichlet-Gamma hierarchy admits a bootstrap that satisfies the principle exactly: $S^{IBNP}_i = X^{obs}_i (1-W_i)/W_i$ with $W_i \sim \mathrm{Beta}(c\hat{F}_{I-i}, c(1-\hat{F}_{I-i}))$ sampled directly from its predictive distribution. Only the allocation proportion $W_i$ is simulated; the observed triangle is held fixed. It thus inherits calibration from any development-proportion method (Chain-Ladder, Bornhuetter-Ferguson, Cape Cod, or other), making it model-agnostic.
The coverage deficit is $O(I^{-1/2})$, independent of the number of development periods. Under compound Poisson data-generating processes the bootstrap is conservative for every $F_{I-i} \in (0,1)$: the predictive standard deviation analytically exceeds the true value by the factor $1/\sqrt{F_{I-i}}$.
The ODP bootstrap violates the principle through two mechanisms in opposite directions: re-estimation inflates bootstrap variance under the ODP DGP, while missing accident-year frailty deflates it under frailty DGPs. The resulting coverage discrepancy is $Ω(1)$ regardless of $I$, providing a structural explanation for the cross-portfolio miscalibration heterogeneity documented by Meyers (2015).
Chain-Ladder, Bornhuetter-Ferguson and Cape Cod emerge as credibility estimators under diffuse, informative and pooling priors respectively, with identical structure for counts and amounts. The concentration $c$ serves as a diagnostic: $\hat{c} < 30$ signals non-stationary development.
Published: 2026-05-15 12:09:47
Authors: Daria Blinova, Gayathri Emuru, Rakesh Emuru, Kushagradheer Shridheer Srivastava, Mina Rulis, Sunita Chandrasekaran, Benjamin E. Bagozzi
Categories: cs.CL
Abstract:
This paper introduces a dataset of interlinked multimodal political communications from the Russian government, addressing persistent deficiencies in the availability of social text- and image-based data for authoritarian politics contexts. The dataset comprises two large corpora of official speeches delivered by senior actors within the Kremlin and the Russian Ministry of Foreign Affairs over multiple decades. For each speech, we provide Russian- and English-language texts, associated images and captions where available, and harmonized metadata including (e.g.) dates, speakers, (geo)locations, and official government content tags. Unique identifiers link images to speeches and align Russian and English versions of the same communication texts. We further augment these linked datasets with validated topical annotations for both speech texts and speech images, which are generated via transformer-based multimodal topic modeling and refined by a Russian politics expert. The resulting data resources support multimodal, multilingual, temporal, and/or spatial analyses of (authoritarian) political communication and offer a valuable testbed for social science research and large language model (LLM) applications in political domains.
Published: 2026-05-15 11:55:53
Authors: Tomoya Nakatani
Categories: math.AG, math.AC, math.RT
Abstract:
We discuss a triangulated category of graded matrix factorizations of a deformed polynomial associated to the $A_μ\textrm{-}$singularity. The semi-universal deformation of the $A_μ\textrm{-}$singularity is given by a certain deformation of the polynomial of type $A_μ$. In this paper, we consider the category of graded matrix factorizations associated to this deformed polynomial for a fixed parameter. To do so, we introduce a formal variable to make the polynomial homogeneous. As our main result, we construct a full strongly exceptional collection in this category for a generic parameter.
Published: 2026-05-15 11:53:22
Authors: Jiafeng Liu, Wenhui Zhou, Xinming Pei, Yifan Peng, Huamin Wang, Yin Yang, Lei Lan, Weiwei Xu
Categories: cs.GR
Abstract:
Affine Body Dynamics (ABD) within the Incremental Potential Contact (IPC) framework provides accurate simulation of extremely stiff solids exhibiting near-rigid behavior, with strict non-penetration guarantees. However, IPC's globally coupled barrier constraints hinder scalable execution across multiple GPUs and compute nodes. We propose a distributed formulation of ABD using a consensus-based ADMM scheme. Each compute node solves its local ABD subproblem in parallel, followed by a global consensus step that enforces consistency among shared boundary bodies. The proposed method preserves IPC-level robustness and global consistency under distributed execution. Experiments demonstrate stable convergence, non-penetration, and efficient scaling on large-scale scenes across multiple nodes.
Published: 2026-05-15 11:36:01
Authors: Wenjie Yang, Hang Yu, Yuyu Guo, Peng Di
Categories: cs.CV
Abstract:
In this work, we address the critical yet underexplored challenge of symmetric multimodal-to-multimodal (MM2MM) retrieval, where queries and contexts are interchangeable. Existing universal multimodal retrieval works struggle with this task, as they are constrained by the labeled asymmetric datasets used. We produce SOLAR (Self-supervised jOint LeArning for symmetric multimodal Retrieval), a novel two-stage self-supervised framework that leverages readily available unlabeled web-scale image-text pairs. Based on the observation that both semantic alignment and discrepancies exist between two modalities, in the first stage, we learn the intersection mask of image-text pair, allowing us to align intersection while preserving semantic of difference. In the second stage, the learned mask is further utilized to construct positive and hardnegative samples via masking different parts of image/text, which enable us to conduct self-supervised multimodal embedding learning. Complementing this framework, we present a new benchmark featuring high-quality human-verified positive and hard-negative pairs to evaluate symmetric MM2MM retrieval under realistic conditions, as well as the corresponding pipeline. Extensive experiments against ten SOTA methods show SOLAR surpasses the strongest supervised VLM by 7.08 points on this benchmark, with over 50x fewer model parameters and a 5x smaller embedding dimension. Code and benchmark will be available soon.
Published: 2026-05-15 11:27:09
Authors: Wenwen Liu, Zhang Shuang
Categories: quant-ph, physics.optics
Abstract:
Non-Hermitian physics enables novel control over open quantum and wave systems, but selectively isolating individual modes without delicate balancing of gain and loss remains challenging. Here we introduce a gauge-engineering method in directed-graph networks that support geometry-protected pure decay modes-eigenstates exhibiting smooth exponential amplitude decay along directed paths. In fully connected configurations, a single dominant mode naturally emerges with a large, tunable energy gap from the rest. By adding synthetic gauge fields via phase-compensated non-reciprocal hopping, we can promote any desired pure decay mode to the dominant position, while preserving its amplitude profile. The approach extends to simultaneous selection of paired modes in half-connected graphs and customizable multi-mode distributions in higher dimensions via orthogonal folding. Our method enables robust, loss/gain-free control over mode profiles, advancing applications in single-mode lasers, sensors, and quantum processing.
Published: 2026-05-15 11:02:16
Authors: Janne Rotter, Pau Benazet i Montobbio, Davinia Hernández-Leo
Categories: cs.CY, cs.AI, cs.HC
Abstract:
In recent years, generative AI (GenAI) in educational settings has become ubiquitous in students' daily lives, despite its potential to induce over-reliance, metacognitive disengagement, and diminished learning when used unrestrictedly. While most prior research has thus focused on how to pedagogically scaffold its usage, the question of when to allow off-the-shelf GenAI remains understudied and lacks pedagogically grounded empirical investigation. We treat access timing itself as a form of implicit scaffolding and operationalize it through a reinforcement learning (RL) agent that decides when students should access GenAI, with a reward function grounded in metacognitive theory, cognitive load theory, and productive failure. In a mixed-methods controlled lab study with N=105 participants, we compared the agent's effect on learning gains and metacognitive engagement to unrestricted and fully restricted use. Results show that strategically timed GenAI access under the reinforcement learning condition improved objective post-test performance and metacognitive accuracy compared with unrestricted access, while reducing task errors and time on task relative to complete withholding, all without the need for explicit metacognitive prompts or structured scaffolding. However, no between-condition differences emerged on self-reported metacognitive awareness. Overall, timing of GenAI access therefore is a tractable, theoretically grounded, and scalable pedagogical paradigm that improves over completely unrestricted and withheld access, compatible with off-the-shelf tools and potentially low adoption barrier. This opens up a new research area that explores how access timing can be facilitated by educators and implemented in human-AI learning system design.
Published: 2026-05-15 10:59:54
Authors: Taiki Ishigaki, Ko Ayusawa, Eiichi Yoshida
Categories: cs.RO
Abstract:
This paper presents a novel framework for Jacobian computation in motion optimization problems involving multi-link systems, where physical quantities are represented using higher-order time derivatives. In motion optimization of robots and humans, cost functions may incorporate higher-order time derivatives, such as jerk or the time variation of forces, to capture smoothness and perceptual characteristics, particularly in motion skill analysis and expressive behaviors, thereby necessitating Jacobian computations involving these quantities. However, such Jacobians are typically computed using numerical or automatic differentiation without explicitly exploiting the underlying multi-link structure, which can lead to increased computational cost and numerical instability. To address this limitation, we propose a structured Jacobian formulation for motion optimization, based on the comprehensive motion computation framework, in which physical quantities and their higher-order time derivatives are systematically represented along the multi-link structure. The proposed method systematically derives analytical expressions for Jacobians of kinematic and dynamic quantities, including momentum, forces, and joint torques, with respect to generalized coordinates and their higher-order derivatives. The resulting framework is applicable to both direct and inverse optimization. Through numerical experiments, we demonstrate that the proposed method improves computational efficiency compared to numerical and automatic differentiation, while achieving comparable accuracy. Furthermore, we demonstrate its effectiveness in inverse optimization by recovering cost function weights from motion data. Together, these results indicate that the proposed formulation provides a scalable and structured computational foundation for motion optimization involving higher-order time derivatives in multi-link systems.
Published: 2026-05-15 10:56:39
Authors: J. H. Xu, Z. L. Wang
Categories: physics.flu-dyn
Abstract:
In this study, we develop an interface-contact simulation framework based on physical criteria and machine-learning-assisted classification to describe coalescence and bouncing within a unified formulation. The framework realizes interfacial coalescence and bouncing through the fusion and generation of multiple volume-of-fluid fields. When adjacent interfaces are predicted to coalesce, multiple VOF fields are collapsed into a single VoF field. When approaching interfaces are predicted to bounce, a single VOF field is regenerated into multiple VOF fields, allowing the interfaces to continue evolving independently. With this treatment, the difficulties associated with topological transition, regime-map identification, increasing computational demand, and stochastic behavior during interfacial approach are separated from the interface-tracking procedure. These decisions are instead assigned to a physics-guided machine-learning model with strong adaptability. This strategy avoids the direct resolution of an ultrathin gas film and reduces the dependence on empirical molecular-force parameters. Simulations of droplet--droplet collisions show that the proposed framework can reproduce both coalescence and bouncing over different impact conditions. By further introducing a drainage-time criterion, the framework is extended to the simulation of droplet impact on a liquid surface. For this problem, the numerical results agree well with both previous experimental observations and the present experiments. Moreover, the framework captures the complete sequence of bouncing followed by subsequent coalescence within a single simulation, These results demonstrate that the proposed framework has strong adaptability for interfacial contact problems and provides a unified modeling route for droplet coalescence, bouncing.
Published: 2026-05-15 10:38:29
Authors: Viktoriia Kornich
Categories: cond-mat.supr-con
Abstract:
We show theoretically that a superconducting critical temperature can be effectively increased in a high-entropy mixture of electrons belonging to conduction and valence bands. In order to employ the entropy of mixing into the superconducting phase dynamics, we suggest to use a metallic trap that removes quasiparticle excitations from the superconductor. This makes the concentration of Cooper pairs a dynamic variable of the entropy of mixing, and thus affects the Ginzburg-Landau functional of the superconductor effectively reducing the first expansion coefficient or, in other words, increasing the critical temperature.
Published: 2026-05-15 10:36:15
Authors: Júlia Orteu Aubach, Fabio Banchelli, Marc Clascà Ramírez, Marta Garcia-Gasulla
Categories: cs.PF, cs.LG
Abstract:
This work extends a framework for predicting the performance of High-Performance Computing (HPC) workloads using Machine Learning (ML). A common limitation in performance modeling is the restricted number of hardware counters that can be collected simultaneously. To address this, we propose a heuristic-based methodology to merge execution traces from multiple runs, each instrumented with a different set of hardware counters. Our approach matches computation bursts across executions by analyzing MPI structure, timing, and communication patterns. This process enables the construction of a unified dataset that includes a wider set of hardware features without relying on multiplexing. The output is a new synthetic trace with all merged counters, which can be used both for HPC performance prediction and for conventional performance analysis. The methodology has been validated on MareNostrum5 machine with a range of kernels and real applications. Results show that the merged counters maintain acceptable accuracy depending on the application, and can be directly used to train ML models on a richer feature space without prior counter selection.
Published: 2026-05-15 10:25:22
Authors: Mahmoud A. Zaky
Categories: math.NA
Abstract:
We introduce a fractional approximation framework for functions with limited regularity near the terminal point. The proposed basis is constructed by composing classical Jacobi polynomials with an endpoint algebraic mapping, thereby incorporating the terminal singular structure directly into the approximation space. The main structural properties of the fractional polynomials are established, including orthogonality relations, derivative identities, and a singular Sturm--Liouville eigenvalue formulation. We then introduce the associated weighted Sobolev spaces and prove projection and Gauss-type interpolation error estimates in weighted norms. Inverse inequalities and weighted Sobolev embedding estimates are also derived. The resulting theory provides a rigorous foundation for high-order spectral and collocation approximations of endpoint-singular and weakly regular problems, including terminal value problems, fractional differential equations, and weakly singular Volterra integral equations.
Published: 2026-05-15 10:19:29
Authors: Chuan-Yang Xing, Bin Zhu
Categories: hep-ph
Abstract:
We propose a near-cutoff parallel-plate waveguide haloscope for sub-meV dark matter. The concept retains the large-area openness of a dish antenna while providing cavity-like field enhancement through slow-wave response and coherent accumulation, without relying on a closed standing-wave resonance. For a copper waveguide, the projected dark photon sensitivity reaches $\varepsilon\simeq2.1\times10^{-15}$ near $m_{A'}\simeq 0.1\,\mathrm{meV}$. With an external magnetic field, the same transducer can approach QCD axion parameter space. The waveguide haloscope highlights a sensitive and scalable route toward future sub-meV bosonic dark matter searches.
Published: 2026-05-15 10:18:25
Authors: Travis Gagie
Categories: cs.DS
Abstract:
The positional Burrows-Wheeler Transform (PBWT) is commonly used to store haplotype panels compactly in such a way that, given a query haplotype, we can quickly find the set maximal exact matches (SMEMs) between the query and the haplotypes in a panel. There are generally two steps in this process: first we find the maximal substrings of the query that occur in the same positions in haplotypes in the panel and then, for each such substring, report the haplotypes in the panel in which the substring occurs in the same position as in the query. Very recently, Bonizzoni, Gagie and Gao (2026) gave two time-space tradeoffs for the second step: they use either $O ((r + h) \log n)$ bits and $O (\log \log \min (h, \ell) + k)$ time to report $k$ haplotypes in the panel, or $O (r \log h + h \log n)$ bits and $O (k \log \log h)$ time, where $r$ is the number of runs in the panel's PBWT and $h$, $\ell$ and $n = h \ell$ are the panel's height, length and size, respectively. We observe here that if we can batch queries until we have found $r \lg (h) / \lg r$ such substrings and we report an average of at least $\lg (r) / \lg h$ haplotypes in the panel per substring, for example, then for the second step we can easily use $O (r \log h)$ bits and constant time to report each haplotype.
Published: 2026-05-15 10:00:21
Authors: Kai Hidajat
Categories: cs.LG
Abstract:
Neural operators excel as deterministic surrogates, but inevitably collapse to the conditional mean when applied to stochastic PDEs, discarding the variance and tail structure upon which uncertainty quantification depends. Recovering this structure typically requires Monte Carlo rollouts or grafted generative models, both of which surrender the one-shot efficiency and resolution invariance that define the operator paradigm. To resolve this, we draw on the Doob-Meyer theorem, which establishes that any semimartingale fundamentally decomposes into a predictable drift and an unpredictable, zero-mean martingale. Translating this theorem into an architectural prior, we introduce the Martingale Neural Operator (MNO). MNO maps an initial condition directly to the conditional mean and covariance of the terminal law, parameterized by a drift-like mean and a low-rank factor $B_φ$ with $B_φ^\top B_φ$ positive semi-definite by construction. For our experiments, we use a Gaussian residual instantiation. Across 1D SPDEs, rough volatility, and 2D operator tasks, MNO reduces Wasserstein distance by up to $120\times$ on $φ^4$ field theory and $68\times$ on stochastic Burgers, evaluating $\sim 3\times$ faster than a conditional diffusion baseline at matched wall-clock training budgets. On 2D tasks, MNO is comparable to FNO on zero-shot resolution transfer and turbulent flow, while quasi-deterministic systems such as Gray-Scott remain a failure mode.
Published: 2026-05-15 09:58:57
Authors: Ricardo Venâncio, Clarisse Sousa, Filipe Duarte, Luís Ribeiro
Categories: cs.CR
Abstract:
This paper analyzes the security of the Message Queuing Telemetry Transport (MQTT) protocol in the context of the Internet of Things (IoT). The main objective consists of identifying vulnerabilities and proposing security improvements. Adopting a hybrid methodology, a theoretical review was combined with an experimental demonstration in a simulated Smart Home environment. Eavesdropping, Tampering, Denial of Service (DoS), and Brute Force attacks were executed and analyzed. The results evidenced critical risks due to the absence of robust encryption and authentication. Finally, mitigation strategies and best practices are proposed to strengthen MQTT implementations.
Published: 2026-05-15 09:56:34
Authors: Tong Chen, Joshua Slone, Gustavo Amorim, Pamela A. Shaw, Bryan E. Shepherd, Thomas Lumley
Categories: stat.ME
Abstract:
In regression models fitted to data from complex survey designs, sampling weights often incorporate non-essential variation, inflating variance estimates. Stabilized weights mitigate this issue by adjusting sampling weights to account for variation explained by covariates. In the context of two-phase sampling, we evaluate the performance of optimal stabilized weights and propose combining the stabilized weight estimator with generalized raking, a class of efficient design-based estimators. This combination improves efficiency by reducing unnecessary weight variation and leveraging information from auxiliary variables. We show this combination can be implemented using the standard statistical package that handles two-phase samples and generalized raking. Simulation studies demonstrate that the proposed estimator enhances precision under realistic two-phase designs, though efficiency gains may be limited in highly informative designs. The developed methods were applied to a large multinational two-phase study of Kaposi sarcoma among people living with HIV.
Published: 2026-05-15 09:56:16
Authors: Hanneke Scheppink, Rainer Herpers, Jordy Thielen, Ivan Volosyak
Categories: q-bio.NC
Abstract:
A code-modulated motion visual evoked potential (c-MVEP) for brain-computer interfacing (BCI) is presented in this study. This paradigm uses pseudo-random sequences to visually stimulate objects using motion as an alternative to flickering. In an offline experiment of this study, EEG data were recorded and compared during sequential stimulation of a single object under four conditions: c-MVEP, code-modulated visual evoked potential (c-VEP), steady-state motion visual evoked potential (SSMVEP), and steady-state visual evoked potential (SSVEP). c-MVEP showed similar time-domain characteristics as c-VEP, and also in the frequency domain c-MVEP evoked a broadband response similar to c-VEP, with a comparable signal-to-noise ratio (SNR), albeit more focused in the lower frequency range. Both SSMVEP and SSVEP showed clear oscillatory responses at the stimulation frequency and harmonics, with a higher SNR for SSVEP than SSMVEP. The spatial distribution of c-MVEP showed the main activation at Oz and spread across multiple electrodes, whereas c-VEP showed less spreading and was more focused at Oz. Similar observations were made for SSMVEP and SSVEP. From subjective ratings, there was no clear preference for the motion-based stimulation of SSMVEP or c-MVEP over flicker-based stimulation of SSVEP or c-VEP. The online experiment of this study, evaluated a 4-class BCI with the same four conditions, testing the practical feasibility of the c-MVEP paradigm. The c-MVEP BCI reached a mean accuracy of 85.67% with an average selection time of 2.61s, which was significantly lower than c-VEP (97.81%; 1.15s) and SSVEP (93.42%; 1.94s), but significantly higher than SSMVEP (64.91%; 4.18s). Overall, this study shows the great potential of the newly proposed c-MVEP paradigm using motion stimulation for BCI applications, providing a valuable alternative to the c-VEP paradigm using flickering stimulation.