April 2026 arXiv papers — page 22

Ujunwa Mgboh, Rafi Ibn Sultan, Joshua Kim, Kundan Thind

Learning-based fluence map prediction offers a fast alternative to iterative inverse planning in intensity-modulated radiation therapy (IMRT), but its robustness under realistic distribution shifts remains unclear. We study a two-stage transformer pipeline that maps anatomy (CT and contours) to dose and then to beamlet fluence maps. We compare fluence-stage

Maria Bras-Amorós, Nathan Kaplan, Deepesh Singhal

We study statistical properties of random numerical semigroups of a given genus. We analyze the graph of a typical numerical semigroup, understood as a function from $\mathbb{N}$ to $\mathbb{N}$. If $S$ is a numerical semigroup of genus $g$, this leads us to consider the collection of points $\left(\frac{k-1}{g-1},\frac{a_k(S)}{g} \right)$ where $1 \le k \le

Anthony Kable, Melissa Mills, David J. Wright

We show the subgroup of 20 nonzero fourth powers in the finite field of order 81 is a cap set. Similarly, the subgroup of 9 nonzero seventh powers in the field of order 64 is a cap set. These are the cases related to the card games of SET and EvenQuads, and both are known to be maximal cap sets. A corollary is that the cosets of these subgroups form a partit

João Araújo, Wolfram Bentz, Michael Kinyon, Janusz Konieczny António Malheiro

We introduce the inverse monoid of inner partial automorphisms of a semigroup -- a tool that associates to every semigroup an inverse semigroup. When the semigroup is a group, this inverse semigroup is isomorphic to the group of inner automorphisms with a zero adjoined. We then describe this structure for completely simple semigroups, the full transformation

R. A. Borzi, E. S. Loscar, S. A. Grigera

We study the nearest-neighbor spin-ice model subjected to a magnetic field applied along the global [111] and [110] directions, focusing on the role of sample geometry in stabilizing topological phase transitions. While no Kasteleyn transition is expected for this field orientations in the thermodynamic limit, we show that constraining the transverse dimensi

Wouter van Doorn

Revisiting a $50$-year-old estimate of Choi, Erd\H{o}s and Szemer\'edi, we show that if $A \subseteq \{1, 2, \ldots, 2n\}$ satisfies $|A| \ge n + 1.2 \cdot 10^8$, then there exist five distinct integers whose pairwise sums are all contained in $A$. In order to guarantee pairwise sums of three or four integers instead, we show that one can replace the constan

B. F. L. Ward, S. Jadach, W. Placzek, M. Skrzypek

We present recent developments in precision high energy collider physics based on the IR-improvement of unintegrable singularities in the infrared regime via amplitude-based resummation in $QED\times QCD \subset SU(2)_L \times U_1 \times SU(3)^c$. We focus on specific applications relevant to precision observables in LHC/FCC, LC, CLIC, CEPC, and CPPC physics

Nicolas Crampé, Quentin Labriet, Lucia Morey, Satoshi Tsujimoto

The rank two Jacobi algebra $\mathfrak{J}_2$ is identified as the dynamical algebra of the generic quadratic superintegrable model on the two-sphere. The physical representation of this algebra is obtained from its embedding in $\mathfrak{su}(1,1)^{\otimes 3}$. The exact solution of the model is derived algebraically from this representation. The wavefunctio

Alina Chertock, Alexander Kurganov, Lorenzo Micalizzi, Nan Zhang

We propose a new second-order asymptotic-preserving (AP) dual formulation finite-volume (DF-FV) method for the thermal rotating shallow water (TRSW) equations. The TRSW system models geophysical flows characterized by horizontal temperature/density variations, exhibiting multi-scale dynamics due to the coexistence of fast rotational waves and slower advectiv

Nayoung Choi, Haeyu Jeong, Changbong Kim, Hongjun Lim

Behavioral logs provide rich signals for user modeling, but are noisy and interleaved across diverse intents. Recent work uses LLMs to generate interpretable natural-language personas from user logs, yet evaluation often emphasizes downstream utility, providing limited assurance of persona quality itself. We propose a hierarchical framework that aggregates u

Makoto Takeuchi

Music genre classification shapes how listeners discover music, how platforms design recommendations, and how sociologists study cultural taste. Yet existing genre labels are inconsistent in granularity: they exaggerate boundaries between overlapping categories and hide sociologically important heterogeneity within broad labels. Cultural sociologists have lo

Diany Pressato, Honghao Tan, Mariam Elmoazen, Shin Hwei Tan

Developers are increasingly overwhelmed by AI-generated issue reports that lack actionability and reproducibility, eroding trust in automated bug detection tools. In this paper, we present IssueSpecter, an automated tool that finds bugs in uncovered code segments and automatically generates prioritized, actionable issue reports. IssueSpecter combines coverag

Emre Ardıç, Yakup Genç

Federated learning is a machine learning paradigm in which multiple devices collaboratively train a model under the supervision of a central server while ensuring data privacy. However, its performance is often hindered by redundant, malicious, or abnormal samples, leading to model degradation and inefficiency. To overcome these issues, we propose novel samp

Amir Fernández Ouaridi

We develop a structure theory for transposed Poisson algebras over fields of characteristic different from two. In particular, we prove that every finite-dimensional transposed Poisson algebra over an algebraically closed field decomposes as the direct sum of a unital ideal and a nilpotent ideal. As a consequence, we obtain restrictions on simple transposed

Neeraj Varshney, Steve Blandino, Jian Wang, Anuraag Bodi

ISAC is currently being standardized within the 3GPP New Radio (NR) to enable cellular infrastructure to perform sensing using existing communication waveforms. While standardization is progressing, practical deployment may be limited by scenario-dependent observability constraints. For example, in UMa-AV scenarios, sensing with a single TRP can be affected

Mahsa Sanei, Fernando Moreu

Rebar inspection in reinforced concrete construction requires sustained awkward postures and complex mental mapping of two-dimensional drawings onto three-dimensional assemblies. This study evaluated an Augmented Reality (AR)-assisted rebar inspection system deployed on Microsoft HoloLens 2 through a within-subjects experiment with 30 participants. Full-body

Austine Oloo, Zainab Altaweel, Yohei Hayamizu, Peiqi Liu

Current robots are capable of computing plans to accomplish complex tasks. However, real-world environments are inherently open and dynamic, and unforeseen situations frequently arise during plan execution, such as jamming doors and fallen objects on the floor. These situations may result from the robot's own action failures or from external disturbances, su

Ban Q. Tran, Duong M. Chu, Hai T. D. Pham, Viet Q. Nguyen

Quantum Machine Learning (QML) has recently emerged as a highly promising research frontier. Within this domain, Quantum Neural Networks (QNNs),characterized by Variational Quantum Circuits (VQCs) at their core and featuring layers of quantum gates optimized by classical algorithms, have garnered significant attention. However, a rigorous and exhaustive eval

R. Simpson, C. Zülch, K. B. Ng, I. Belosevic

Tricationic protactinium monofluoride ($^{229}$PaF$^{3+}$) has been proposed as a candidate for probing physics beyond the Standard Model of particle physics. Since studies with $^{229}$PaF$^{3+}$ require significant experimental advances, we exploit the stable, valence-isoelectronic dicationic cerium monofluoride (CeF$^{2+}$) as a surrogate. Gas-phase fluor

Rémi Martin, Sandrine Duluard, Céline Merlet

Electrophoretic deposition is a method of choice for generating coatings thanks to its ease of implementation and its ability to produce coatings of relatively large thicknesses in a single step process. While this process also benefits from a large number of tunable parameters to adapt the coating to each application (applied electric field, particle concen

Tosin Adewumi, Martin Karlsson, Lama Alkhaled, Marcus Liwicki

We introduce a novel task of digital battery passport (DBP) conformance classification and introduce the first public benchmark for the task: BatteryPass-12K, created synthetically from real pilot samples. This is as the EU's battery regulation on DBPs comes into effect soon and there exists no public dataset. We evaluated 22 language models (LMs) in zero-sh

Yann Pouillon, Bill Clintone Oyomo, James Sifuna, María Camarasa-Gómez

We present an efficient and accurate implementation of hybrid exchange-correlation (XC) functionals in the SIESTA code, enabling large-scale simulations based on Hartree-Fock-type exact exchange combined with strictly localized numerical atomic orbitals (NAOs). Our approach exploits a fitted representation of the NAOs in terms of Gaussian-type orbitals (GTOs

Muhammad Kaleem Ullah Khan

Agriculture is a key sector of the economies of developing countries. It serves as a primary source of income and employment for rural populations. However, each year, a large portion of crops is wasted because of pests and diseases. Well-timed prediction of plant diseases is crucial to sustainable, high-quality agricultural production. Detection of plant di

Guangnian Ji, Yash Kulkarni, Stéphane Zaleski

Atomization stretches and folds the liquid-gas interface before fragmenting it into ligaments and droplets, making fractal measures a natural descriptor of the breakup state. We examine this idea in two-dimensional volume-of-fluid direct numerical simulations, VOF-DNS, of a liquid jet with adaptive mesh refinement in Basilisk. Box counting of the full resolv

Tom Liptay, Dan Schwarz, Rafael Poyiadzi, Jack Wildman

Forecasting benchmarks produce accuracy leaderboards but little insight into why some forecasters are more accurate than others. We introduce Bench to the Future 2 (BTF-2), 1,417 pastcasting questions with a frozen 15M-document research corpus in which agents reproducibly research and forecast offline, producing full reasoning traces. BTF-2 detects accuracy

Zihao Ding, Beining Wu, Jun Huang, Shiwen Mao

We investigate task-success-oriented resource allocation for federated split learning (FSL) at the wireless edge. In this setting, the server must jointly determine bandwidth, transmit power, split-layer placement, compression level, and terminal participation under per-round deadline, memory, and spectrum constraints. These coupled decisions affect wireless

Friedrich Martin Schneider, Andreas Thom

We prove that the unit group of a non-discrete irreducible, continuous ring, in the sense of John von Neumann, does not admit any non-trivial unitary representation continuous with respect to the strong operator topology.

Zhongkai Yu, Haotian Ye, Chenyang Zhou, Ohm Rishabh Venkatachalam

All current LLM serving systems place the GPU at the center, from production-level attention-FFN disaggregation to NVIDIA's Rubin GPU-LPU heterogeneous platform. Even academic PIM/PNM proposals still treat the GPU as the central hub for cross-device communication. Yet the GPU's compute-rich architecture is fundamentally mismatched with the memory-bound natur

Rishikesh Gajjala

Christoph, Dragani\'{c}, Gir\~{a}o, Hurley, Michel, and M\"{u}yesser conjectured that, when $d\mid n$, the expected number of cycles in a uniformly random cycle-factor of a directed $d$-regular graph on $n$ vertices is uniquely maximised by the disjoint union of $n/d$ copies of the complete looped digraph $K_d^\circ$, with value $(n/d)H_d$ [FOCS 2025]. We di

Zhiyang Wang, Fei Yang, Long-Qing Chen

Polar skyrmions in ferroelectric superlattices are nanoscale topological polarization textures typically regarded as weakly coupled objects confined to individual layers, with a role secondary to that of the underlying symmetry-breaking order parameter. Here using large-scale phase-field simulations of ferroelectric superlattices, we uncover a hidden thermal

Abhay K. Ram, Efstratios Koukoutsis, George Vahala, Kyriakos Hizanidis

Can quantum computers effectively simulate the propagation and scattering of electromagnetic waves in a classical plasma? This chapter introduces some of the basic concepts in mathematics and physics essential to answering that question. The numerical simulations of Maxwell equations for wave propagation in dielectrics are constrained by technological limita

Alain Giresse Tene, Thomas Konrad

We present a variational algorithm for fault tolerant quantum computing to solve a system of linear equations which directly maximises the parameters of the target fidelity. This so-called measurement test algorithm can be applied to any computational task with a solution that is represented as eigenvector of a self-adjoint matrix. The solution is prepared a

Jiahong Wang, Logan Numerow, Stelian Coros, Christian Theobalt

Graph neural networks (GNNs) have emerged as a versatile and efficient option for modeling the dynamic behavior of deformable materials. While GNNs generalize readily to arbitrary shapes, mesh topologies, and material parameters, existing architectures struggle to correctly predict the temporal evolution of key physical quantities such as linear and angular

Nikita Dobronravov

A version of the Uncertainty Principle says: There does not exist a non zero function in $L_p(\mathbb{R}^d)$ if its Fourier transform is supported by a set of finite $\alpha$-Hausdorff measure with $\alpha<2d/p$. This UP does not hold at the endpoint $\alpha=2d/p$. We find the sharp form of the UP in the limit case. We prove that there exists a non-zero func

Yiwei Shi, Zixing Song, Mengyue Yang, Cunjia Liu

{Closed-loop inverse source localization and characterization (ISLC) requires a mobile agent to select measurements that localize sources and infer latent field parameters under strict time constraints.} {The core challenge lies in the belief-space objective: valid uncertainty estimation requires expensive Bayesian inference, whereas using fast learned belie

Bowen Cai, Weiheng Bai, Youshui Lu, Haoran Xu

As blockchain ecosystems grow, financially motivated attackers increasingly exploit decentralized finance (DeFi) protocols, causing frequent and severe losses. Unlike conventional cyberattacks, DeFi exploits propagate rapidly due to the transparent and composable nature of smart contracts. We identify a critical pattern, Imitative Attack Cascade: an initial

Vaibhav Mohanty, Sunghan Ro

Diffusion with multipole-moment conservation gives rise to transport laws that generalize Fick's law and has attracted growing attention following experimental advances in strongly tilted optical lattices. It was recently shown that conserving complete multipole-moment groups leads to subdiffusive dynamics governed by a nonlinear diffusion equation, raising

T. J. Barton, Chris Constantakis, Patti Hauseman, Annie Mous

We study reliability in autonomous language-model agents that translate user mandates into validated tool actions under real capital. The setting is DX Terminal Pro, a 21-day deployment in which 3,505 user-funded agents traded real ETH in a bounded onchain market. Users configured vaults through structured controls and natural-language strategies, but only a

Liangyu Wu, Qibin Liu, Marco Toliman Lucchini, Julia Gonski

Dual-readout calorimeters achieve superior energy resolution by simultaneously measuring Cherenkov and scintillation signals for event-by-event electromagnetic fraction correction, making them attractive for next-generation Higgs factories. However, if a full waveform readout is required for time-based analysis to separate Cherenkov and scintillation signals

Tapas Bar, David Pesquera, Arnau Villalobos-Martin, Cristian Rodriguez-Tinoco

Thin ferroelectric BaTiO$_3$ films often exhibit continuous transitions instead of the first-order behavior of bulk crystals, a discrepancy usually attributed to epitaxial strain or dimensionality. Using quasi-adiabatic nanocalorimetry on freestanding BaTiO$_3$ membranes-free of clamping and substrate heat sinking-we show that domain morphology, not thicknes

Harsh Doshi, Shreerang Pande, Sathish K. Sukumaran, Apratim Chatterji

In Escherichia coli (E. coli), entropic repulsion between the two daughter DNA ring polymers under cylindrical confinement is believed to be an important factor governing chromosomal segregation. The repulsion can be enhanced by topological modifications, i.e., by the introduction of internal loops at certain locations along the contour of the circular DNA.

Christian Kuehn, Jaeyoung Yoon

We study self-attention dynamics on the unit sphere as an interacting particle system arising from an idealized Transformer-type update. Under a symmetry assumption on weight matrices given by $Q^\top K=V=V^\top$, the flow admits a gradient-flow structure and an exact reformulation in the eigenbasis of $V$, revealing a spectral mode-selection mechanism. We s

Rahul Harsha Cheppally, Sidharth Rai, Sudan Baral, Benjamin Vail

Accurate fruit maturity identification is essential for determining harvest timing, as incorrect assessment directly affects yield and post-harvest quality. Although ripening is a continuous biological process, vision-based maturity estimation is typically formulated as a multi-class classification task, which imposes sharp boundaries between visually simila

Surabhi S Nath, Vindula Jayawardana, Monica Van, Matt Klenk

The creative design process involves transforming abstract goals into concrete outcomes through a series of decisions made under constraints. While such processes are commonly shaped by feedback like rewards, their impact on design decision making remains unclear. To better understand the role of rewards in the design process, we modeled a 3D parametric, goa

Leonardo Ivan Estrella Dzib, James Holehouse

The evolutionary origins of structural features in reconstructed gene-regulatory networks (GRNs) remain poorly understood, especially given the random aspects of gene expression. Here, we extend a classical model of GRN evolution to allow a single network to express a distribution of phenotypes through noisy developmental dynamics. Inspired by Hopfield netwo

Martha Cash, Charlotte Fowler, Alexander M. Wyglinski

This paper analyzes the role of time-series clustering in traffic matrix (TM) prediction. Traffic flows within a TM often exhibit heterogeneous behavior, which can reduce the effectiveness of global forecasting models that predict all flows jointly. To address this, we propose a clustering-based prediction framework that groups flows into smaller subsets and

Haoran Wan, Yaxiong Xie, Kyle Jamieson

Current and future applications demand ultra-low latency and consistent throughput, yet frequently traverse 5G cellular networks, so cope with volatile packet dynamics, as 5G base station schedulers dynamically react to user workloads and wireless channel conditions. The task of evaluating network algorithms in these environments is hamstrung by current tool

Weiyi Kong, Ahmad Mohammad Saber, Amr Youssef, Deepa Kundur

In modern energy systems, industrial control systems (ICS) and power-system SCADA require intrusion detection that is not only accurate but also auditable by operators. The ICS intrusion-detection landscape is currently dominated by established supervised detectors. In this paper, we study whether an off-the-shelf large language model (LLM) can serve as a co

Karim Alghoul, Hussein Al Osman, Abdulmotaleb El Saddik

Photoplethysmography (PPG) is increasingly used in wearable affective computing due to its low cost and ease of integration into consumer devices. Recent advances in deep learning have introduced long-range sequence models, such as Transformers, and state-space models, like Mamba, which have demonstrated strong performance on natural language and general tim

A. Runov, V. Angelopoulos, A. V. Artemyev, X. An

The electron fluxes at energies $E>$100\,keV are shown to be vanishing in the quiet time plasma sheet at geocentric distance of 60 Earth's radii (R$_E$) where the Moon traverses the magnetotail. Fluxes of energetic electrons up to relativistic energies were, however, observed during disturbed space weather conditions. In this paper, we study the data collect

Shouxu Lin, Zhiyuan Guo, Jiaxin Lin

LLM inference is constrained by GPU memory capacity and bandwidth. Tiered memory architectures mitigate this by allowing the GPU to offload memory to the remote tier. However, existing memory offloading frameworks rely on prefetching data into local GPU HBM. This approach underutilizes system resources by introducing HBM contention, squandering memory capaci

Teetat Pipattaratonchai, Aueaphum Aueawatthanaphisut

Industrial chemical plants often operate under strict data confidentiality constraints, making centralized data-driven process modeling difficult. Federated learning (FL) provides a promising solution by enabling collaborative model training across distributed facilities without sharing raw operational data. This paper proposes a privacy-preserving federated

Feven Markos Hunde, Wojciech A. Hellwing, Maciej Bilicki

The annihilation of dark matter (DM) particles is expected to produce Standard Model particles, providing a potential indirect signature of DM. The clumpy substructure of DM haloes amplifies the expected annihilation signal, an effect commonly quantified by the subhalo boost factor. Standard semi-analytic models usually treat this boost as a universal functi

Alexander Kalinowski

Representational collapse, where embeddings become anisotropic and lose multi-scale structure, can erode downstream performance long before performance metrics react. We propose an online, topology-aware monitor for evolving neural representations that couples Modular Morse Homology Maintenance (MMHM) with a composite Collapse Index (CI). Instead of rebuildi

Blake Andrews, Xiao Liu, Raphael Zumbrunn, Calvin Lee

Zero-field (ZF) nuclear magnetic resonance (NMR) spectroscopy probes scalar J-couplings between nuclei while dispensing with large homogeneous magnetic fields, enabling low-cost and geometrically flexible detection, including through conductive enclosures. Despite these advantages, its broader use for chemical analysis has been limited by sensitivity and by

Tianhao Lei, Parsa Esmaeilkhani, Saghir Alfasly, Wataru Uegami

Foundation models are reshaping computational histopathology, yet their value for whole-slide image retrieval relative to strong patch-based and supervised aggregation baselines remains unclear. We benchmarked ten pipelines on 9,387 diagnostic slides spanning 17 organs and 60 diagnoses from The Cancer Genome Atlas (TCGA) using patient-level leave-one-patient

Alexander Kalinowski

We study detection of collapse in high-dimensional point clouds, where mass concentrates near a lower-dimensional set relative to a non-collapsed geometry. We propose persistent homology-based test statistics under two well-studied filtrations, with cutoffs calibrated under a broad set of non-collapsed reference models. We benchmark power across three altern

Zaid Nasser, Mikhail Iumanov, Tianhao Li, Maxim Popov

We present RADIO-ViPE (Reduce All Domains Into One -- Video Pose Engine), an online semantic SLAM system that enables geometry-aware open-vocabulary grounding, associating arbitrary natural language queries with localized 3D regions and objects in dynamic environments. Unlike existing approaches that require calibrated, posed RGB-D input, RADIO-ViPE operates

Leandro Di Bella, Adrian Munteanu, Bruno Cornelis

Generative motion prediction must satisfy three simultaneous requirements for real-world autonomy: high accuracy, diverse multimodal futures, and strictly bounded latency. Diffusion models meet the first two but violate the third, requiring tens to hundreds of denoising steps. We identify a conditioning strategy that resolves this tension: \textit{single-ste

A. S. Spivak, V. N. Temlyakov

The main goal of this paper is twofold. First, we extend some results known in the case of weak greedy algorithms with a scalar parameter to the case of weak greedy algorithms with a weakness sequence. Second, we formulate a new setting of the problem of convergence of greedy algorithms. Usually, we are interested in convergence of an algorithm for all eleme

Luyun Lin, Lixing Lin, Zhen Zhang, Moxuan Zheng

Traditional moving average convergence divergence (MACD) trading rules are often constrained by signal lag and susceptibility to false signals. To address these limitations, this study develops a volume-price-adjusted MACD (VP-MACD) framework that incorporates volume, volatility, and intraday price structure into the conventional indicator, and introduces a

Ronald Deng, Samuel McCauley, Aidin Niaparast, Helia Niaparast

Algorithms with predictions is a growing area that aims to leverage machine-learned predictions to design faster beyond-worst-case algorithms. In this paper, we use this framework to design a learned data structure for the incremental strongly connected components (SCC) problem. In this problem, the $n$ vertices of a graph are known a priori and the $m$ dire

Gabriel Rondón, Paulo R. da Silva, Jaume Llibre

We study limit cycles in piecewise complex systems with switching manifold $\mathbb{S}^1$. Using M\"obius transformations we establish an equivalence between circular and straight-line discontinuities that preserves periods, stability, and algebraic structure. For piecewise polynomial holomorphic systems we obtain lower bounds on the number of limit cycles v

Bernard Asamoah Afful, Luis F. Gordillo

Black Sigatoka disease (BSD), also known as black leaf streak disease, is an airborne fungal infection caused by \textit{Pseudocercospora fijiensis} that severely impacts global banana and plantain production. Its persistence and resistance to eradication make it one of the most challenging plant diseases to manage. In this paper, we propose a deterministic

Sachin Sarode, Chetan Patil, Vinayak Joshi

In this paper, we define and study $S$-Noetherian lattices as a natural generalization of Noetherian rings. We prove that a ring $R$ is $S$-Noetherian if and only if its ideal lattice, $Id(R)$, is $S_L$-Noetherian. Furthermore, we establish a Cohen-Kaplansky type theorem for $S$-Noetherian lattices, showing that $L$ is $S$-Noetherian if and only if every $S$

Jaskirat Sudan, Hashim Ali, Surya Subramani, Hafiz Malik

Supervised contrastive learning (SupCon) is widely used to shape representations, but has seen limited targeted study for audio deepfake detection. Existing work typically combines contrastive terms with broader pipelines; however, the focus on SupCon itself is missing. In this work, we run a controlled study on wav2vec2 XLS-R (300M) that varies (i) similari

Yufei Wu, Tamás Szűcs, Agnes Moors, Francis Tuerlinckx

Evidence accumulation models (EAMs) provide a powerful framework for inferring latent cognitive processes from choice and reaction time data. While EAMs are traditionally limited to binary choices, recent developments have extended them to rotationally symmetric continuous responses via the circular diffusion model \citep{smith2016diffusion} and the spatiall

Lawrence Ein, Wenbo Niu, Jinhyung Park

The main goal of this paper is to study some local and global properties of secant varieties of algebraic curves. These results complement our previous work [8] by addressing issues given therein and providing solutions to problems raised subsequently. Specifically, we show a description of tangent cones of secant varieties of curves, and compute the cohomol

Rustam Galimullin, Hermine Grosinger, Munyque Mittelmann

Autonomous agents acting in realistic Multi-Agent Systems (MAS) should be able to adapt during their execution. Standard strategic logics, such as Alternating-time Temporal Logic (ATL), model agents' state- or history-dependent behaviour. However, the dynamic treatment of agents' available actions and their knowledge of required actions is still rarely addre

Aliasghar Arab, Milad Khaleghi, Koorosh Aslansefat

This paper presents a systematic verification and validation (V\&V) framework for the Evasive Minimum Risk Maneuver (EMRM) feature in autonomous vehicles, addressing a critical gap in existing safety assessment methods. We introduce the first formally integrated pipeline that unifies Hazard Analysis and Risk Assessment (HARA), System-Theoretic Process Analys

Anthony Bloch, Sebastián J. Ferraro, David Martín de Diego, Shreyas Bharadwaj

Discrete variational methods show excellent performance in numerical simulations of mechanical systems. In this paper, we adapt discrete variational integrators for the case of mechanical systems with double-bracket dissipation. In particular, we will work with forced Euler-Poincar\'e and forced Lie-Poisson systems, and the case of interest for us will be wh

Richard A. A. Jonker, Bárbara Maria Ribeiro de Abreu Martins, Sérgio Matos

This paper presents a principled and scalable framework for systematically generating complex Question Answering (QA) data. In the core of this framework is a graphlet-anchored generation process, where small subgraphs from a Knowledge Graph (KG) are used in a structured prompt to control the complexity and ensure the factual grounding of questions generated

Phillip C. Lotshaw, Titus Morris, Stuart Hadfield, Ryan Bennink

Approximate combinatorial optimization is a promising use case for quantum computers. The quantum optimization algorithms often employ a fixed ansatz that evolves an unbiased initial state towards states with better values of the optimand, then samples the states to determine an approximately optimal solution. However, promising alternative approaches have c

Sven Hirsch, Yipeng Wang

We give a counterexample to a recently conjectured variant of the Penrose inequality.

David Blanco, Guillem Pérez-Nadal, Bruno Sivilotti

The Euclidean formulation of quantum gravity can be interpreted in terms of a probability distribution over Riemannian manifolds. In the context of de Sitter gravity, the statistics of the total volume according to this distribution is encoded in the dependence of the partition function on the cosmological constant. We use this observation to obtain a probab

Maria F. Gamal'

A contraction $T$ on a (complex, separable) Hilbert space is stable, or of class $C_{0\cdot}$, if $T^n\to 0$ in the strong operator topology. It is proved that for a non-stable pure subnormal contraction $T$ there exists a singular inner function $\theta$ such that the range of $\theta(T)$ is not dense. Consequently, $T$ has nontrivial hyperinvariant subspac

Md Shifatul Ahsan Apurba, Md Selim, Jin Chen

The use of CT imaging is important for screening, diagnosis, therapy planning, and prognosis of lung cancers. Unfortunately, due to differences in imaging protocols and scanner models, CT images acquired by different means may show large differences in noise statistics, contrast, and texture. In this study, we develop a novel conditional MeanFlow pipeline fo

Alireza Goldar, Zhen Qin, Zhihui Zhu, Zhe-Xuan Gong

Broad claims about whether adaptivity helps in quantum state tomography can be misleading unless the state family, measurement architecture, and error metric are specified carefully. We study a restricted but physically important regime: single-copy quantum state tomography under local Pauli basis measurements, where the allowed measurement settings are tens

Abhilasha Dave, Semin Joung, SangKyeun Kim, Ramon Reed

In this work, we demonstrate the deployment of a hardware-accelerated machine learning (ML) inference system integrated into a real-time processing at the DIII-D tokamak fusion reactor. The team has successfully deployed an AMD/Xilinx KCU1500 field-programmable gate array (FPGA) into the realtime Plasma Control System (PCS) nodes that receives the live Beam

Rodrigo García Arancibia, Pamela Llop, Mariel Lovatto

In this paper we propose a semiparametric spatial autoregressive model that combines a linear covariate component with a nonparametrically estimated spatial term, allowing flexible dependence modeling without restrictive covariance structure while preserving interpretability. We establish asymptotic properties, including consistency and asymptotic normality,

Lucas T. Braydwood, Phillip C. Lotshaw

Variational Quantum Algorithms, including the Quantum Approximate Optimization Algorithm (QAOA), have shown promise in solving optimization problems but rely on costly variational loops that can themselves be hard optimization problems. Many methods have been proposed to mitigate this variational cost, with one of the most common being parameter transfer and

Vyom Sharma, Debajyoti Datta

The optimal kernel configuration for Mixture-of-Experts (MoE) inference depends on both batch size and the expert routing distribution, yet production systems dispatch from batch size alone, leaving 10-70% of kernel throughput unrealized. We present RaMP, a routing-aware dispatch framework. A performance-region analysis derives, from hardware constants alone

Lorenz Eberhardt

The super Virasoro minimal string is defined by coupling spacelike and timelike super Liouville theories on the worldsheet. There are four different theories 0A$^\pm$ and 0B$^\pm$ depending on discrete choices on the worldsheet. We show that these theories arise naturally from quantization of 3d supergravity, and the amplitudes compute the dimension ($+$) or

Kartick Ramakrishnan, Phani Motamarri

Accurate large-scale Kohn-Sham density functional theory (DFT) calculations are essential for modeling complex material systems, including interfaces, defects, nanoclusters, and twisted two-dimensional heterostructures. Achieving chemical accuracy at scales of $10^4$-$10^5$ electrons with practical time-to-solution, however, remains challenging for existing

María Florencia Acosta, Rodrigo García Arancibia, Pamela Llop, Mariel Lovatto

This paper presents a novel value-aware approach to product recommendation that simultaneously addresses the high dimensionality and sparsity of user-item data while explicitly incorporating the contribution of each product and user to overall sales revenue. The proposed framework encodes revenue contributions in the user-item matrix and computes customer si

Ronaldo Garcia, Shmuel Mark Helman, Dan Reznik

We prove that over a generic Poncelet triangle family, the locus of the circumcenter of an inversive triangle is a conic. Additionally, we prove an earlier conjecture: over generic Poncelet triangles, two unique points exist which maintain constant power with respect to the circumcircle and Euler's circle of the family, respectively.

Andrea Addazi, Giuseppe Meluccio

The microscopic origin of the de Sitter entropy remains a central puzzle in quantum gravity related to the cosmological constant problem. Within $\mathcal{H}$olographic $\mathcal{N}$aturalness, we propose this entropy is carried by light, coherent degrees of freedom - "hairons" - emerging as moduli of gravitational instantons on orbifolds. From the Euclidean

A. Krech, A. Boyarintsev, B. Grynyov, N. Karavaeva

This study presents the development and the characterization of novel white reflective filaments suitable for additive manufacturing of finely segmented plastic scintillators. The filament is based on polycarbonate (PC) and polymethyl methacrylate (PMMA) polymers loaded with titanium dioxide (TiO$_2$) and polytetrafluoroethylene (PTFE) to enhance reflectivit

Andrea Addazi, Salvatore Capozziello, Giuseppe Meluccio

The cosmological constant (CC, $\Lambda$) problem represents a remarkable discrepancy of about 120 orders of magnitude between the observed dark energy and its natural expectation from quantum field theory. This paper synthesizes two paradigms - holographic naturalness ($\mathcal{HN}$) and pre-geometric gravity (PGG) - to propose a unified resolution. The $\

Chang Liu, Henghui Ding, Nikhila Ravi, Yunchao Wei

This report summarizes the objectives, datasets, and top-performing methodologies of the 2026 Pixel-level Video Understanding in the Wild (PVUW) Challenge, hosted at CVPR 2026, which evaluates state-of-the-art models under highly unconstrained conditions. To provide a comprehensive assessment, the 2026 edition features three specialized tracks: the MOSE trac

Dan Edidin, Arun Suresh

The orthogonal beltway problem is the problem of recovering the $\mathrm{O}(n)$-orbit of a $\delta$-function supported at a finite number of points in $\r^n$ from its auto-correlation or, equivalently, second moment. It was introduced as a generalization of the classical beltway problem in X-ray crystallography and was motivated by cryo-electron microscopy.

Youngsoo Baek, Samuel I. Berchuck

We consider the problem of scalable sampling algorithms to fit Bayesian generalized linear mixed models on large datasets. Stochastic gradient Langevin dynamics, coupled with smooth re-parameterizations of variance parameters, produces divergent Markov chains and cannot be reliably used for sampling covariance parameters of random effects. We advocate the us

Chun Wang Chau, Wojciech J. Jankowski, Bo Peng, Robert-Jan Slager

We unveil that non-Abelian multigap band topology characterized by nontrivial Euler class invariants induces observable magnetononlinear Hall transport phenomena. We demonstrate these effects in a highly-tunable class of recently synthesized two-dimensional kagome N-heterocyclic carbene (NHC) metal-organic frameworks. We showcase the controllability of the n

Idir Arab, Milto Hadjikyriakou, Paulo Eduardo Oliveira

We study stochastic ordering of system lifetimes with dependent and heterogeneous components whose marginal distributions are obtained through transformations of a common baseline. The dependence structure is modeled via Archimedean copulas, allowing for a unified treatment of several transformation-based models, including proportional hazard, proportional r

Fateme Taraghi, Atefe Aghaei, Mohsen Ebrahimi Moghaddam

Despite significant advances in facial recognition systems, they remain vulnerable to face presentation attacks. Among them, disguise makeup attacks are particularly challenging, as they use advanced cosmetics, prosthetic components, and artificial materials to realistically alter facial appearance, often making detection difficult even for humans. Despite t

Taylor Maxson, Roberto Corizzo, Yaning Wu, Nathalie Japkowicz

Class-level evaluation can conceal substantial performance disparities across subconcepts within the same class, causing models that perform well on average to fail on specific subpopulations. Prior work has shown that common evaluation measures for imbalanced classification are biased toward larger minority subconcepts and that utility-based reweighting usi

Mattia Garatti, Luca Nenna, Simona Rota Nodari, Luca Tamanini

We studied the Benamou--Brenier formulation of the Schr\"odinger problem, focusing on a gap between theoretical results and applications, that often involve measures with unbounded support. While the existing proof in the literature relies on the compactness of the marginals' supports to ensure the necessary regularity of the Schr\"odinger potentials, we ext

Yikun Wang, Idit Zehavi, Sergio Contreras, Giovanni Aricò

Baryonic physics is anticipated to be a major source of systematic uncertainty in current and future large-scale cosmological surveys. We investigate how baryonic effects on halo density profiles vary with secondary halo properties at fixed halo mass, using the large-volume MillenniumTNG hydrodynamical simulation and its dark matter-only counterpart. We focu

Victor Varentsov

To enhance the quality of pulsed cold ion beams extracted from the two cryogenic stopping cells at FAIR (i.e., the one currently used in the FRS and the one under development for the Super-FRS at FAIR), we propose using a novel gas-dynamic ion beam extraction and bunching technique as an alternative to the radiofrequency quadrupole (RFQ) method. This techniq

Dongyoung Lee, Sehwan Kim, Jisoo Kyoung

Metasurfaces have revolutionized compact wavefront control using planar, subwavelength structures. However, conventional meta-optical devices predominantly operate within a far-field paradigm, assuming electromagnetic decoupling between the source and metasurface, which limits control to post-emission wavefront shaping. Here, we define and experimentally dem