April 2026 arXiv papers — page 127

Buse Şen, Yifan Hu, Daniel Kuhn

We introduce Multistage Conditional Compositional Optimization (MCCO) as a new paradigm for decision-making under uncertainty that combines aspects of multistage stochastic programming and conditional stochastic optimization. MCCO minimizes a nest of conditional expectations and nonlinear cost functions. It has numerous applications and arises, for example,

Laurence Bonat, Francesco Tonini, Elisa Ricci, Lorenzo Vaquero

Semantic Multi-Object Tracking (SMOT) extends multi-object tracking with semantic outputs such as video summaries, instance-level captions, and interaction labels, aiming to move from trajectories to human-interpretable descriptions of dynamic scenes. Existing SMOT systems are trained end-to-end, coupling progress to expensive supervision, limiting the abili

Gergő Szalay, Gergely Zsolt Kovács, Sándor Teleki, Balázs Pintér

Resolving and rewriting references is fundamental in programming languages. Motivated by a real-world decompilation task, we abstract reference rewriting into the problems of direct and indirect indexing by permutation. We create synthetic benchmarks for these tasks and show that well-known sequence-to-sequence machine learning architectures are struggling o

Yihe Li, Gregory J. Duck

Iterators are a fundamental programming abstraction for traversing and modifying elements in containers in mainstream imperative languages such as C++. Iterators provide a uniform access mechanism that hides low-level implementation details of the underlying data structure. However, iterators over mutable containers suffer from well-known hazards including i

Ishrak AlhajjHassan

We establish finite-step probabilistic upper bounds on the contraction ratios $\rho_k = \Delta_{k+1}/\Delta_k$ for iterated Pearson correlation dynamics. Let $(P_k)_{k\ge 0}$ be the sequence generated by the Pearson update. Define $\Delta_k := \|P_{k+1}-P_k\|_F$, $\rho_k := \Delta_{k+1}/\Delta_k$ for $\Delta_k > 0$, and $\delta_k := \Delta_k/n$. Although $\D

Tony Rost

Governance opacity over AI systems shifts in kind as capability asymmetry grows, and the strongest forms defeat the disclosure-based remedies governance ordinarily relies on. This paper applies a six-dimension framework from political theory (legitimacy, accountability, corrigibility, non-domination, subsidiarity, institutional resilience) to six AI governan

Francesco Tonini, Alessandro Conti, Lorenzo Vaquero, Cigdem Beyan

Human-Object Interaction (HOI) detection is a longstanding computer vision problem concerned with predicting the interaction between humans and objects. Current HOI models rely on a vocabulary of interactions at training and inference time, limiting their applicability to static environments. With the advent of Multimodal Large Language Models (MLLMs), it ha

S. Dwivedi

In this paper, we study non-reflexive Banach spaces $X$ for which the quotient space $X^{**}/X$ is reflexive. Such spaces were first introduced by James R.~Clark, where they were called coreflexive spaces. We show that a space $X$ is coreflexive if and only if every separable subspace $Y\subseteq X$ is coreflexive, provided that $X$ is w$^*$-sequently dense

Hanno Sahlmann, Waleed Sherif

We study physical (near-kernel of constraints) states of 4-d Euclidean loop quantum gravity in Smolin's weak coupling limit on the complete graph $K_5$ using variational Monte Carlo with neural network quantum states. We investigate the Hamilton constraint $\hat{H}$ in the ordering proposed by Thiemann, as well as $\hat{H}^\dagger$ and $\hat{H}+\hat{H}^\dagg

Göksel Daylan Esmer, Saliha Türkmen

We explore hidden symmetries in electrically charged, four-dimensional rotating Kerr-Newman black hole within $f(R)$ gravity. By deriving the Killing and Killing-Yano tensors, we establish their role in the spacetime structure. The gyromagnetic ratio is calculated and shown to retain its universal value of $g = 2$, consistent with all four-dimensional black

Stephen Raharja, Toshiharu Sugawara

We propose novel particle swarm optimization (PSO) variants incorporated with deep neural networks (DNNs) for particles to pursue globally optimal positions in dynamic environments. PSO is a heuristic approach for solving complex optimization problems. However, canonical PSO and its variants struggle to adapt efficiently to dynamic environments, in which the

Y. Zhang, N. Labrosse, T. A. Kucera, S. Parenti

The first dedicated observation of an off-limb prominence by the Spectral Imaging of the Coronal Environment (SPICE) instrument took place on April 15, 2023. We aim to create parameter maps on the prominence region, including temperature, pressure, and column mass, by studying the integrated intensity of the Lyman $\beta$ and Lyman $\gamma$ lines from SPICE

Jiun Tian Hoe, Weipeng Hu, Xudong Jiang, Yap-Peng Tan

Human-Object Interaction (HOI) modelling captures how humans act upon and relate to objects, typically expressed as <person, action, object> triplets. Existing approaches split into two disjoint families: HOI generation synthesises scenes from structured triplets and layout, but fails to integrate mixed conditions like HOI and object-only entities; and HOI e

Jingbo Liu

We show that the maximum expected inner product between a random vector and the standard normal vector over all couplings subject to a mutual information constraint or regularization is equivalent to a truncated integral involving the rate-distortion function, up to universal multiplicative constants. The proof is based on a lifting technique, which construc

Lev Razumovskiy, Nikolay Karenin

This paper provides a systematic comparison between Fitted Dynamic Programming (DP), where demand is estimated from data, and Reinforcement Learning (RL) methods in finite-horizon dynamic pricing problems. We analyze their performance across environments of increasing structural complexity, ranging from a single typology benchmark to multi-typology settings

Dominic Stafford

A first search is presented for BSM resonances in four top quark production in the 2 lepton channel, using $138\mathrm{fb}^{-1}$ $pp$ data collected at $\sqrt{s}=13$ TeV, and $35\mathrm{fb}^{-1}$ $\sqrt{s}=13.6$ TeV $pp$ data. No significant excess is observed; limits are set on vector Z', scalar, pseudoscalar and ALP mediators. Z' mediators with 50% width a

Huaijing Huang, Daowen Qiu, Le Luo, Paulo Mateus

Recently, Dunjko et al.(PRL, 2018) proposed an algorithm for accelerating the solution of 3-satisfiability problems using a small-scale quantum computer. In this paper, we design a distributed quantum-classical hybrid algorithm for solving K-satisfiability problems. Under resource-constrained conditions, our algorithm achieves a significant acceleration in t

Michiel Gautama, Faezeh Khodabandehlou, Christian Maes, Ion Santra

We investigate the thermal responses of a harmonic oscillator chain coupled at its boundaries to heat baths held at different temperatures. This setup sustains a steady energy flux, continuously dissipating heat into both reservoirs. By introducing slow variations in the bath temperatures, we quantify the resulting excess heat currents and thereby obtain the

Jan Kochanowski, Omar Fawzi, Cambyse Rouzé

We define 2-indexed $(q,p)$-Schatten quasi-norms for any $q,p > 0$ on operators on a tensor product of Hilbert spaces, naturally extending the norms defined by Pisier's theory of operator-valued Schatten spaces. We establish several desirable properties of these quasi-norms, such as relational consistency and the behavior on block diagonal operators, assumin

Zhuofeng Li, Yi Lu, Dongfu Jiang, Haoxiang Zhang

The rapid rise in AI conference submissions has driven increasing exploration of large language models (LLMs) for peer review support. However, LLM-based reviewers often generate superficial, formulaic comments lacking substantive, evidence-grounded feedback. We attribute this to the underutilization of two key components of human reviewing: explicit rubrics

Pavel Chizhov, Egor Bogomolov, Ivan P. Yamshchikov

Efficiency and safety of Large Language Models (LLMs), among other factors, rely on the quality of tokenization. A good tokenizer not only improves inference speed and language understanding but also provides extra defense against jailbreak attacks and lowers the risk of hallucinations. In this work, we investigate the efficiency of code tokenization, in par

Fabrizio Spera, Tommaso Boccato, Michal Olak, Sara Cammarota

Recent progress in visual brain decoding from fMRI has been enabled by large-scale datasets such as the Natural Scenes Dataset (NSD) and powerful diffusion-based generative models. While current pipelines are primarily optimized for perception, their performance under mental-imagery remains less well understood. In this work, we study how a state-of-the-art

Anatoliy I. Lotkov, Valerii K. Kozin, Denis V. Kurlov, Jelena Klinovaja

We study $\mathbb{Z}_3$-symmetric Rabi model that describes a three-level system coupled to two bosonic modes. We derive a mapping of the two-mode $\mathbb{Z}_3$ Rabi model onto a qubit-boson ring. This mapping allows us to formulate a realistic implementation of the $\mathbb{Z}_3$ Rabi model based on superconducting qubits. It also provides context for the

Shiteng Cao, Xiaochong Lan, Yuwei Du, Jie Feng

Local life service recommendation is distinct from general recommendation scenarios due to its strong living need-driven nature. Fundamentally, accurately identifying a user's immediate living need and recommending the corresponding service are inextricably linked tasks. However, prior works typically treat them in isolation, failing to achieve a unified mod

Yuri Nesterenko

The long-standing hypothesis formulated by Goreinov, Tyrtyshnikov and Zamarashkin \cite{GTZ1997} has recently been solved positively by Sengupta and Pautov \cite{SP2026} in the case of two-column matrices. In this paper, we complement their elegant proof with the equality criterion.

Benjamin Guiot

We discuss an inconsistency of the eikonal Color Glass Condensate (CGC) description of Deep Inelastic Scattering (DIS). In this framework, the Bjorken-$x$ dependence enters the cross section solely through the rapidity cutoff $Λ=x_b$, leading to an all-order cross section independent of $x_b$. To address this issue, we explore a natural modification in which

Yuhang Dai, Xingyi Yang

Feed-forward 3D reconstruction models are efficient but rigid: once trained, they perform inference in a zero-shot manner and cannot adapt to the test scene. As a result, visually plausible reconstructions often contain errors, particularly under occlusions, specularities, and ambiguous cues. To address this, we introduce Free Geometry, a framework that enab

Huihuang Jiang, Heyang Li, Zifan Wang, Ying Fan

Peer review shapes which scientific claims enter the published record, but its internal dynamics are hard to measure at scale because reviewer criticism and author revision are usually embedded in long, unstructured correspondence. Here we use a fixed-prompt large language model pipeline to convert the review correspondence of \textit{Nature Communications}

Borja Diez, Luis Guajardo

Within the wide variety of topological solitons supported by Yang--Mills theory, merons occupy a particularly distinguished role. Despite their simplicity, they represent genuinely non-Abelian configurations that can be regarded as the fundamental building blocks of instantons, and they provide a qualitatively accurate picture of confinement. In this work, w

Yiheng Sun, Zhenyu Zhang, James Jun He

The supercurrent diode effect (SDE), characterized by unequal critical currents in opposite directions, has been observed with or without magnetic fields, yet mechanisms enabling zero-field SDE without explicit symmetry breaking remain underexplored. Here we investigate a Josephson junction with strong electron-electron interaction modeled by a Hubbard $U$ t

Xiaohe Li, Jiahao Li, Kaixin Zhang, Yuqiang Fang

While Multimodal Large Language Models (MLLMs) excel in general vision-language tasks, their application to remote sensing change understanding is hindered by a fundamental "temporal blindness". Existing architectures lack intrinsic mechanisms for multi-temporal contrastive reasoning and struggle with precise spatial grounding. To address this, we first intr

Yong Zhang, Nicolas Labrosse, Susanna Parenti, Therese A. Kucera

The first dedicated observation of an off-limb prominence by Solar Orbiter took place on April 15, 2023. Our aim is to determine the range of different physical parameters of this prominence and to examine how these parameters affect the formation of the Lyman $\beta$ and Lyman $\gamma$ lines of hydrogen. We have found a way to refine key physical parameters

Huaning Liu, Lirong Guo, Zilong Liu

This work is concerned with the fundamental scaling laws of quasi-complementary sequence sets (QCSSs) by understanding how large the set size (denoted by $M$) can grow with the flock size ($K$) and the sequence length ($N$). We first establish a geometric framework that transforms a QCSS into a complex unit-norm codebook, through which and by exploiting the

Xiaomin Li, Tala Wang, Zichen Zhong, Ying Zhang

Daily scenarios are characterized by visual richness, requiring Multimodal Large Language Models (MLLMs) to filter noise and identify decisive visual clues for accurate reasoning. Yet, current benchmarks predominantly aim at evaluating MLLMs' pre-existing knowledge or perceptual understanding, often neglecting the critical capability of reasoning. To bridge

Gabriel Frey

We prove a lower bound on the Calabi functional for degenerations of polarized varieties, involving the difference of CM degrees between generically isomorphic families. This may be viewed as a discretely valued version of Donaldson's lower bound for models, in the sense of non-Archimedean geometry. In particular, this generalizes a result of Donaldson, who

Magnus Callsen, Jens H. Nyhegn, Kristian Knakkergaard Nielsen, Georg M. Bruun

The interplay between charge and spin dynamics is at the heart of strongly correlated materials. Inspired by recent quantum simulation experiments, we develop a conserving diagrammatic method to describe the Fermi-Hubbard model for strong repulsion and small hole doping away from the half-filled anti-ferromagnetic ground state. We show that doping leads to f

Sebastiano Della Lena, Alessio Muscillo, Paolo Pin

Consumers discover their preferences through experience, yet the sequence and composition of those experiences are often designed by firms, digital platforms, or policymakers. We introduce a ``data-design'' framework for preference discovery, in which the structure of consumption data shapes learning. Bundling generates correlated exposure across goods, so u

Massimo Bartoletti, Angelo Ferrando, Enrico Lipparini, Vadim Malvone

Smart contracts deployed on blockchains such as Ethereum routinely manage large amounts of assets, making their security critical. Empirical studies show that real-world attacks often exploit flaws in the business logic of contracts that unfold across multiple transactions, such as liquidity or front-running attacks. Detecting these attacks requires reasonin

Ziyang Wang

Satellite constellations are transforming space systems from isolated spacecraft into networked, software-defined platforms capable of on-orbit perception, decision making, and adaptation. Yet much of the existing AI studies remains centered on single-satellite inference, while constellation-scale autonomy introduces fundamentally new algorithmic requirement

Zhangchi Chen, Zihao Ye, Weizhe Zheng

We study the distribution modulo one of linear recurrent sequences of real numbers. We prove criteria for the finiteness of the set of limit values of the fractional parts of such a sequence and give lower bounds for the maximal distance between two limit values. Our results generalize theorems of Flatto, Lagarias, Pollington, and Dubickas.

Kavya Gupta, Nektarios Kalampalikis, Christoph Heitz, Isabel Valera

Fairness in algorithmic decision-making is often defined in the predictive space, where predictive performance - used as a proxy for decision-maker (DM) utility - is traded off against prediction-based fairness notions, such as demographic parity or equality of opportunity. This perspective, however, ignores how predictions translate into decisions and ultim

João Bettencourt, Sérgio Guerreiro

Recent advances in Generative Artificial Intelligence, particularly Large Language Models (LLMs), have stimulated growing interest in automating or assisting Business Process Modeling tasks using natural language. Several approaches have been proposed to transform textual process descriptions into BPMN and related workflow models. However, the extent to whic

Graziano Crasta, Virginia De Cicco, Annalisa Malusa

We introduce a family of (nonlinear) pairing measures that ensure the validity of the divergence rule for composite functions $\boldsymbol{B}(x,u(x))$, where $\boldsymbol{B}(\cdot,t)$ is a bounded divergence-measure vector field, and $u$ is a scalar function of bounded variation. The elements of the family depend on the choice of the pointwise representative

Gitesh Malik

Reinforcement learning has shown promise for automating power-grid operation tasks such as topology control and congestion management. However, its deployment in real-world power systems remains limited by strict safety requirements, brittleness under rare disturbances, and poor generalization to unseen grid topologies. In safety-critical infrastructure, cat

Matthew Collinson

We investigate several categories related to transition structures, using a mixture of algebraic and topological methods. We show how two such categories are connected by a contravariant adjunction. This is the most detailed of a family of such results depending on topological restrictions on objects and morphisms.

Zhe Huang, Peng Wang, Yan Zheng, Sen Song

Product bundling boosts e-commerce revenue by recommending complementary item combinations. However, existing methods face two critical challenges: (1) collaborative filtering approaches struggle with cold-start items owing to dependency on historical interactions, and (2) LLMs lack inherent capability to model interactive graph directly. To bridge this gap,

N. Ouahioune, D. Hoff, P. K. Maroju, C. L. Arnold

Photoionization of atoms by high-order harmonics in the presence of a laser may lead to quantum interference from which information about the photoionization dynamics or the light fields can be extracted. Traditionally, this interference arises from two-photon transitions involving the absorption of consecutive harmonics combined with the absorption and stim

Qianyu Chen, Shujian Yu

Functional magnetic resonance imaging (fMRI) is widely used for studying and diagnosing brain disorders, with functional connectivity (FC) matrices providing powerful representations of large-scale neural interactions. However, existing diagnostic models are trained either on a single site or under full multi-site access, making them unsuitable for real-worl

Kieran A. Murphy

We propose InfoChess, a symmetric adversarial game that elevates competitive information acquisition to the primary objective. There is no piece capture, removing material incentives that would otherwise confound the role of information. Instead, pieces are used to alter visibility. Players are scored on their probabilistic inference of the opponent's king l

Yilmaz Ege Gonul, Baris Taskin

Oscillator-based Ising/Potts machines (OIMs/OPMs) are promising hardware accelerators for NP-hard combinatorial optimization problems using coupled oscillator synchronization dynamics. Analog OIMs/OPMs offer speed advantages but have limited coupling resolution, process variation susceptibility, and scalability issues, while digital GPU/CPU emulations provid

Servet B. Bayraktar, Andreas Orthey, Marc Toussaint

Object extraction tasks often occur in disassembly problems, where bolts, screws, or pins have to be removed from tight, narrow spaces. In such problems, the distance to the environment is often on the millimeter scale. Sampling-based planners can solve such problems and provide completeness guarantees. However, sampling becomes a bottleneck, since almost al

Weijie Wang, Qihang Cao, Sensen Gao, Donny Y. Chen

Reconstructing 3D representations from 2D inputs is a fundamental task in computer vision and graphics, serving as a cornerstone for understanding and interacting with the physical world. While traditional methods achieve high fidelity, they are limited by slow per-scene optimization or category-specific training, which hinders their practical deployment and

Benjamin Bakker, Kristin DeVleming, Stefano Filipazzi, Radu Laza

Koll\'{a}r showed that small deformations of elliptically fibered smooth $K$-torsion varieties with $H^2(X,\mathcal{O}_X)=0$ remain elliptically fibered. We extend this result to any fibered smooth $K$-torsion variety $X$ with $H^2(X,\mathcal{O}_X)=0$, using Hodge theoretic techniques and the $T^1$-lifting criterion of Kawamata--Ran. More generally, our stra

Natong Lin, Jiachen Wang, Lisa C. Barry, Song Han

Gait speed is a widely used indicator of functional health and mobility decline, yet in clinical practice it is commonly measured manually using a stopwatch, which limits scalability and measurement frequency. Privacy-preserving and maintenance-free sensing approaches can enable more routine and less burdensome assessments in real-world care settings. This p

Clemens Schmid, Anton Pokusinskyi, Markus Gruber, Corentin Pfaff

The current-driven resistive transition is central to superconducting single-photon detectors, transition-edge sensors, and fluxonic devices. Depending on sample uniformity, dimensions, and heat removal, it can be driven by phase-slip events, flux-flow instabilities (FFI), or normal-domain formation. Here, we investigate the influence of two types of microst

Federica Ferrari, Flavia Davidhi, Bernard Maacaron, Alberto Motta

Maintaining stable internal representations of continuous variables is fundamental for effective robotic control. Continuous attractor networks provide a biologically inspired mechanism for encoding such variables, yet neuromorphic realizations have rarely addressed proprioceptive estimation under resource constraints. This work introduces a spiking ring-att

Mathis Nguyen, Mohamed Ali Lajnef

Modern distributed systems generate large volumes of logs that can be analyzed to support essential AIOps tasks such as fault diagnosis, which plays a crucial role in maintaining system reliability. Most existing approaches rely on log-based models that treat logs as linear sequences of events. However, such representations discard the structural context bet

Fabian Kislat

Lorentz symmetry is the fundamental symmetry of Einstein's theory of Special Relativity and has been tested to great precision. Nevertheless, the possibility remains that it is violated at the Planck scale, as predicted by some theories of quantum gravity. While the Planck scale is not directly accessible to experiments, minute residual deviations from Loren

Aike Yang, Hao Wang

Under interpolation-type assumptions such as the strong growth condition, stochastic optimization methods can attain convergence rates comparable to full-batch methods, but their performance, particularly for SGD, remains highly sensitive to step-size selection. To address this issue, we propose a unified stochastic trust-region framework that eliminates man

Zijian Gao, Wangwang Jia, Xingxing Zhang, Pengfei Qian

Multimodal Continual Instruction Tuning (MCIT) is essential for sequential task adaptation of Multimodal Large Language Models (MLLMs) but is severely restricted by catastrophic forgetting. While existing literature focuses on the reasoning language backbone, in this work, we expose a critical yet neglected dual-forgetting phenomenon across both perception d

Daniel Waltner, Boris Gutkin

Semiclassical methods have been applied very successfully to describe the nontrivial transition from the quantum to the classical regime in $\textit{single}$-particle or at least $\textit{few}$-particle systems. Challenges on the way to an extension to $\textit{many}$-body systems result from the exponential proliferation of the number of classical orbits in

Tim Hansen, Arturo Gomez-Chavez, Ilya Shimchik, Andreas Birk

We promote in this paper the processing of radar data in the frequency domain to achieve higher robustness against noise and structural errors, especially in comparison to feature-based methods. This holds also for high dynamics in the scene, i.e., ego-motion of the vehicle with the sensor plus the presence of an unknown number of other moving objects. In ad

Jianfeng Guo, Shiyu Zhu, Runnong Zhou, Ruwen Wang

The novel kagome family RTi3Bi4 (R: rare-earth) offers a unique platform for exploring distinctive physical phenomena such as anisotropy, spin density wave, and anomalous Hall effect. In particular, the magnetic frustration and behavior of magnetic anisotropy in antiferromagnetic (AFM) kagome materials are of great interest for the fundamental studies and ho

Zekai Lin, Chao Xue, Di Liang, Xingsheng Han

Supervised Fine-Tuning (SFT) of large language models often suffers from task interference and catastrophic forgetting. Recent approaches alleviate this issue by isolating task-critical parameters during training. However, these methods represent a static solution to a dynamic problem, assuming that parameter importance remains fixed once identified. In this

Ryan Shìjié Dù, Momme Hell, Luc Lenain, Fabrice Ardhuin

The Surface Water and Ocean Topography (SWOT) mission provides two-dimensional observations of sea surface height (SSH) at unprecedented spatial resolution, enabling exploration of ocean variability down to scales of $O(10~\mathrm{ km})$. At these scales, however, interpreting SSH variability is challenging because ocean dynamical signals overlap with measur

A. Bianchi, A. C. Green, J. Degallaix, F. A. Feldmann

The performance of mirrors in optical interferometers is critically influenced by their surface quality. Accurate metrology enables mirror surfaces to be characterized through phase maps describing their three-dimensional structure after coating. In this work, we combine Zernike polynomial decomposition and spatial frequency (PSD) analysis with numerical opt

Alan Frieze, Ross Kang, Aditya Raut, Michelle Sweering

Given a graph $G$ and an integer $r\ge 1$, the $r$th power $G^r$ of $G$ is the graph obtained from $G$ by adding edges for all pairs of distinct vertices at distance at most $r$ from each other. We focus on two basic structural properties of the $r$th power of the binomial random graph $G_{n,p}$, namely, the maximum degree $\Delta(G_{n,p}^r)$ and the chromat

A. Armat, S. Mohammad Moosavi Nejad

The magnetic moments and radiative decay widths of heavy baryons belong to a class of interesting experimental observables which provide direct information about the dynamics of strong interactions as well as the properties and the composition structures of heavy baryons. In this work, through a diquark model we compute these two quantities for doubly and tr

Kangsan Kim, Minki Kang, Taeil Kim, Yanlai Yang

Memory-based self-evolution has emerged as a promising paradigm for coding agents. However, existing approaches typically restrict memory utilization to homogeneous task domains, failing to leverage the shared infrastructural foundations, such as runtime environments and programming languages, that exist across diverse real-world coding problems. To address

Wangjie Gan, Miao Pan, Linbo Xi, Wenqi Zhang

Large language models are typically post-trained using supervised fine-tuning (SFT) and reinforcement learning (RL), yet effectively unifying efficient knowledge injection with robust generalization remains challenging. In this work, we provide a training-dynamics analysis showing that SFT can be interpreted as a special case of policy gradient optimization

Lina Nikolaidou, Angeliki Laskari, Tom van Terwisga, Christian Poelma

At the end of 2020, a new cavitation tunnel was commissioned at the Ship Hydrodynamics laboratory of TU Delft, replacing its 1960s predecessor. Since this was a new facility, a flow characterization campaign was performed to investigate the flow quality in the test section. To that end, velocity measurements were performed in the test section using Laser Dop

Davyd Naveriani, Albert Zeyer, Ralf Schlüter, Hermann Ney

Diffusion language models have recently emerged as a leading alternative to standard language models, due to their ability for bidirectional attention and parallel text generation. In this work, we explore variants for their use in speech recognition. Specifically, we introduce a comprehensive guide to incorporating masked diffusion language models (MDLM) an

Vincenzo Amato, Nunzia Gavitone, Rossano Sannipoli

In this paper, given a convex, bounded, open set $\Omega \subset \mathbb{R}^n$ we prove a sharp inequality involving the Laplacian torsional rigidity and both the perimeter and the measure of the domain. Our result generalizes to arbitrary dimensions the inequality established by Makai in the plane which, as conjectured in arXiv:2007.02549. Furthermore, we e

Maya Kayali

The triple-cup product form $\mu$ is a classical invariant of $3$-manifolds, determining the cohomology ring up to torsion. Given a closed, connected, oriented $3$-manifold $M$, we describe an explicit formula for computing $\mu$ from a Heegaard diagram of $M$. Then, we show that the triple-cup product form $\mu$ can be recovered as a reduction of Turaev's h

Lev Razumovskiy, Nikolay Karenin, Mikhail Safro

We study a finite-horizon dynamic pricing problem for event tickets with limited inventory and time-varying demand. The central practical difficulty is that the total demand function $L(t)$ is not observed directly and must be estimated from data, while pricing decisions are sensitive to its temporal shape. The paper examines how the accuracy of this estimat

Djiré Albérick Euraste, Kaboré Abdoul Kader, Jordan Samhi, Earl T. Barr

The lack of transparency about code datasets used to train large language models (LLMs) makes it difficult to detect, evaluate, and mitigate data leakage. We present a perturbation-based method to quantify memorization advantage in code LLMs, defined as the performance gap between likely seen and unseen inputs. We evaluate 8 open-source code LLMs on 19 bench

Kellie Yu Hui Sim, Pin Sym Foong, Darryl Lim, John-Henry Lim

Work on persona-persistent post-mortem agents typically frames design around a life/death binary. This framing neglects a consequential yet under-theorised condition: when individuals remain alive but have impaired decisional capacity. Drawing on a multi-phase workshop in which participants trained and reflected on an AI agent for Advance Care Planning, we e

Muhammad Z. Alam, Larry Stetsiuk, M. Umair Mukati, Zeeshan Kaleem

This paper introduces a novel approach for image and video orientation estimation by leveraging depth distribution in natural images. The proposed method estimates the orientation based on the depth distribution across different quadrants of the image, providing a robust framework for orientation estimation suited for applications such as virtual reality (VR

Enzhuo Zhang, Sijie Zhao, Dilxat Muhtar, Zhenshi Li

Generative diffusion priors have recently achieved state-of-the-art performance in natural image super-resolution, demonstrating a powerful capability to synthesize photorealistic details. However, their direct application to remote sensing image super-resolution (RSISR) reveals significant shortcomings. Unlike natural images, remote sensing images exhibit a

Derek Lilienthal, Manisha Mukherjee, Sameera Horawalavithana

Physical reasoning over visual inputs demands tight integration of visual perception, domain knowledge, and multi-step symbolic inference. Yet even state-of-the-art Vision Language Models (VLMs) fall far short of human performance on physics benchmarks. While post-training algorithms such as Supervised Fine-Tuning (SFT) and Group Relative Policy Optimization

Mohammad Nooraiepour, Zezhang Song, Wei Li, Sarah Perez

Accurate methane sorption prediction across heterogeneous coal ranks requires models that combine thermodynamic consistency, efficient knowledge transfer across data-scarce geological systems, and calibrated uncertainty estimates, capabilities that are rarely addressed together in existing frameworks. We present a physics-informed transfer learning framework

Aleksandr Rubashevskii, Dzianis Piatrashyn, Preslav Nakov, Maxim Panov

Large language models (LLMs) are prone to generating factually incorrect outputs. Recent work has applied conformal prediction to provide uncertainty estimates and statistical guarantees for the factuality of LLM generations. However, existing approaches are typically not prompt-adaptive, limiting their ability to capture input-dependent variability. As a re

Yong Zhang, Nicolas Labrosse, Susanna Parenti, Therese A. Kucera

The first dedicated observation of an off-limb prominence by the Spectral Imaging of the Coronal Environment (SPICE) instrument on board Solar Orbiter took place on April 15, 2023. Our aim is to provide an overview of the potentiality of the diagnostics using these data. We show that we can derive the changes in the physical parameters of the pre-eruptive an

Emir Melliti

Right-reversing is an algorithm used to compute least common multiples in monoids that admit a right-complemented presentation. The algorithm can either terminate and find a result, fail, or run indefinitely. The correctness of the algorithm can be proved with additional assumptions coming from Garside theory. In the same framework, we prove that a non-termi

Mohammad Ahangarkiasari, Andreas Tind Damgaard, Casper Haurum, Kaare B. Mikkelsen

Objective: Investigate whether hypnogram 'realism' can be used to guide an unsupervised method for handling arbitrary types of signal degradation in mobile sleep monitoring. Approach: Combining a pretrained, state-of-the-art 'u-sleep' model with a 'discriminator' network, we align features from a target domain with a feature space learned during pretraining.

Emmanuel Suárez Acevedo, Tiago Ferreira, Kevin Batz, Oliver Bøving

We introduce weighted NetKAT, a domain-specific language for modeling and verifying quantitative network properties. The language is parametric on a semiring, enabling the treatment of a wide range of quantities in a uniform way. We provide a denotational semantics and an equivalent operational semantics, the latter based on a novel model of weighted NetKAT

Charles A. McLemore, Marco Pomponio, Takuma Nakamura, Yifan Liu

A broad and growing array of applications rely on the faithful transmission of ultrastable optical signals over noisy paths, requiring cancellation of environmentally induced noise. A generally accepted limit constrains how well the path length noise can be suppressed in real time. Here, we show that this standard limit is not fundamental and can be improved

Longji He, Elena Emma Wang, Xichun Wang, Juntao Xu

This paper studies a ground-segment implementation problem in 5G non-terrestrial networks (NTN): once UE-side geometric pre-compensation has produced a coarse timing/frequency prior, can an edge-side residual loop keep the uplink inside an NR-feasible operating region under rapid LEO dynamics? We examine this question with a software-defined ground station (

Kittipong Wangnok, Samaporn Tinyanont, Ryan M. Lau, Ryan J. Foley

We present multi-epoch high-resolution optical spectroscopy ($R \simeq 80{,}000$) of the emission-line object NaSt1 to test its proposed binary nature, along with long-term multiband photometry, mid-infrared spectroscopy, and spatially resolved integral field unit (IFU) spectroscopy to probe the circumstellar kinematics of the system. We detect two groups of

Daniel Førland Holmen, Jan Martin Nordbotten, Jon Eivind Vatne

We consider the simplicial de Rham complex and the \v{C}ech-de Rham complex, two bigraded Hilbert complexes whose Hodge-Laplace problems govern spatially coupled problems in mixed dimension and homogeneous dimension, respectively. The former complex can be realized as a subcomplex of the latter. In this paper, we quantify how close these complexes are to eac

Jianlin Xiang, Linhui Dai, Xue Yang, Chaolei Yang

Existing object detectors typically make predictions in a black-box manner and struggle to simultaneously provide discriminative evidence for their predictions, which limits their deployment in safety-critical scenarios. To explain model predictions, existing post-hoc explanation methods mostly rely on gradient-based or perturbation-based operators. These me

Jackie Rao, Ferran Gonzalez Hernandez, Leon Gerard, Alexandra Gessner

Antibody lead optimization is inherently a multi-objective challenge in drug discovery. Achieving a balance between different drug-like properties is crucial for the development of viable candidates, and this search becomes exponentially challenging as desired properties grow. The ever-growing zoo of sophisticated in silico tools for predicting antibody prop

Hussein Abdallah, Ibrahim Abdelaziz, Panos Kalnis, Essam Mansour

Open-world Question Answering (OW-QA) over knowledge graphs (KGs) aims to answer questions over incomplete or evolving KGs. Traditional KGQA assumes a closed world where answers must exist in the KG, limiting real-world applicability. In contrast, open-world QA requires inferring missing knowledge based on graph structure and context. Large language models (

Stefan Höche, Daniel Reichelt

We introduce a technique for the next-to-leading order accurate simulation of $e^+e^-\to W^+W^-b\bar{b}$ that respects the resonant nature of the process above and near the top-quark pair production threshold. The parton-shower evolution, infrared subtraction and NLO matching account in particular for finite width effects beyond the Breit-Wigner structure co

Christos Dounis, Charis Anastopoulos

We study the Tolman-Oppenheimer-Volkoff equation in the presence of a cosmological constant for general thermodynamically consistent equations of state, without imposing regularity at the center. Formulating the problem as an initial value system integrated from an outer boundary inwards, we obtain a general classification of solutions and show that singular

F. Becattini, F. Palli

The mean value of the stress-energy tensor of a given quantum field theory at global thermodynamic equilibrium in a curved space-time can be expressed in terms of the derivatives of the Killing four-temperature field and the derivatives of the metric tensor. Its asymptotic expansion about zero includes an analytic part made of integer powers of these derivat

Robert Kleinberg, Ahan Mishra

In the pinwheel problem, one is given an $m$-tuple of positive integers $(a_1, \ldots, a_m)$ and asked whether the integers can be partitioned into $m$ color classes $C_1,\ldots,C_m$ such that every interval of length $a_i$ has non-empty intersection with $C_i$, for $i = 1, 2, \ldots, m$. It was a long-standing open question whether the pinwheel problem is N

Qinwei Yang, Jingyi Li, Peng Wu, Shu Yang

Randomized controlled trials (RCTs) often suffer from limited inferential efficiency in estimating treatment effects due to their small sample sizes. In recent years, incorporating external controls (ECs) has gained increasing attention as an effective way to augment small RCTs and thereby enhance estimation efficiency. However, ECs are not always comparable

Neeraj Kumar, Ankur Srivastav, Phongpichit Channuie

In this article, we revisit the thermodynamics of Hayward black holes [1] in asymptotic flat spacetime and obtain the bounds on the final mass post merger after head-on collision event of two equal mass black holes. We revisit thermal properties of these black holes from a perspective that the laws of black hole thermodynamics remain valid. Under this condit

Hsien-Chih Chang, Suprovat Ghoshal, Euiwoong Lee

We study the Max-Cut semidefinite programming (SDP) relaxation in the regime where a near-optimal solution admits a low-dimensional realization. While the Goemans--Williamson hyperplane rounding achieves the worst-case optimal approximation ratio $\alpha_{GW}\approx 0.87856$, it is natural to ask whether one can beat $\alpha_{GW}$ when the SDP solution lives