November 2025 arXiv papers — page 18

Haoying Dai

We develop a theoretical framework that aims to link micro-level option hedging and stock-specific factor exposure with macro-level market turbulence and explain endogenous volatility amplification during gamma-squeeze events. By explicitly modeling market-maker delta-neutral hedging and incorporating beta-dependent volatility normalization, we derive a stab

V. A. Li, O. A. Akindele, M. Bondin, S. R. Durham

We first briefly describe the history and motivation behind Cherenkov and scintillation light detection. We then discuss the instrumentation needed to detect these photons as it applies to both photodetectors and readout electronics. One of the motivations is future large neutrino detectors that could in principle differentiate between Cherenkov and scintill

Johannes Hagel

We study the long-term behaviour of the nonlinear, aperiodically and parametrically forced oscillator z'' + z + g(tau) z^2 = 0, g(tau) = y(tau)^(-5/2), where y(tau) is the strictly positive solution of a weakly forced third-order equation. Building on the algebraic invariant constructed in our previous work, we show that the motion of z(tau) is confined to a

Raymond A. Mencia, Taketo Imaizumi, Igor A. Golovchanskiy, Andrea Lizzit

The basic element of circuit quantum electrodynamics (cQED) is a cavity resonator strongly coupled to a superconducting qubit. Since the inception of the field, the choice of the cavity frequency was, with a few exceptions, been limited to a narrow range around 7 GHz due to a variety of fundamental and practical considerations. Here we report the first cQED

Yuchen Hu, Xiaoyi Wang, Long Feng

This paper proposes a novel test method for high-dimensional mean testing regard for the temporal dependent data. Comparison to existing methods, we establish the asymptotic normality of the test statistic without relying on restrictive assumptions, such as Gaussian distribution or M-dependence. Importantly, our theoretical framework holds potential for exte

F. Peißker, M. Zajacek, V. Karas, V. Pavlík

Context. In addition to the supermassive black hole Sgr A*, the inner parsec of our Galactic center is home to numerous fruitful scientific habitats. One of these environments is the S cluster, which consists of two distinct populations: the main-sequence S stars and the dusty G objects. While the majority of the brightest S stars can be classified as young

Maximo T. Estrada

Our work establishes the mathematical equivalence between polymer Self Consistent Field Theory and interfacial SAFT based classical Density Functional Theory by providing an explicit proof that SCFT is the mean field (MF) or saddle point limit of a Legendre dual formulation of iSAFT CDFT. The polymer CDFT ideal chain term is the Legendre conjugate of the sin

Jorge Alberto Garza-Abdala, Gerardo A. Fumagal-González, Daly Avendano, Servando Cardona

Purpose: This study aims to develop and evaluate a three channel denoising diffusion probabilistic model (DDPM) for synthesizing single breast dual view mammograms and to assess the impact of channel representations on image fidelity and cross view consistency. Materials and Methods: A pretrained three channel DDPM, sourced from Hugging Face, was fine tuned

Anders Rantzer

The theory of optimal control on positive cones has recently identified several new problem classes where the Bellman equation can be solved explicitly, in analogy with classical linear quadratic control. In this paper, the idea is extended to minimax adaptive control, yielding exact solutions to instances of the Bellman equation for dual control. In particu

Wenyi Yan, Lu Gan, Hongqing Liu, Shaoqing Hu

We study the fundamental problem of \emph{moduli selection} in the Robust Chinese Remainder Theorem (RCRT), where each residue may be perturbed by a bounded error. Consider $L$ moduli of the form $m_i = \Gamma_i m$ ($1 \le i \le L$), where $\Gamma_i$ are pairwise coprime integers and $m \in \mathbb{R}^+$ is a common scaling factor. For small $L$ ($L = 2, 3,

Jiaying Hong, Ting Zhu, Thanet Markchom, Huizhi Liang

With the rise of AI-generated content (AIGC), generating perceptually natural and feeling-aligned music from multimodal inputs has become a central challenge. Existing approaches often rely on explicit emotion labels that require costly annotation, underscoring the need for more flexible feeling-aligned methods. To support multimodal music generation, we con

Alain Connes, Caterina Consani, Henri Moscovici

We propose and investigate a strategy toward a proof of the Riemann Hypothesis based on a spectral realization of its non-trivial zeros. Our approach constructs self-adjoint operators obtained as rank-one perturbations of the spectral triple associated with the scaling operator on the interval $[\lambda^{-1}, \lambda]$. The construction only involves the Eul

Mario Bukal, Igor Kukavica, Linfeng Li, Boris Muha

We consider the three-dimensional fluid-structure interaction system modeling a system consisting of a viscous incompressible fluid and an elastic plate forming its moving upper boundary. The fluid is described by the incompressible Navier-Stokes equations with a free upper boundary that evolves according to the motion of the structure, coupled via the veloc

Anders Rantzer

A multi-variable adaptive controller is derived as the explicit solution to a minimax dynamic game. The minimizing player selects the control action as a function of past state measurements and inputs. The maximizing player selects disturbances and model parameters for the underlying linear time-invariant dynamics. This leads to a Bellman equation that can b

Zeyuan Li, Wenyi Yan, Lu Gan, Guoquan Li

Conventional analog-to-digital converters (ADCs) fail to capture high-dynamic-range (HDR) signals due to clipping. Modulo ADCs circumvent this limitation by folding the input prior to quantization and algorithmically reconstructing the original waveform. This work presents a field-programmable gate array (FPGA)-based modulo ADC platform for systematic HDR pe

Hristo Papazov, Francesco D'Angelo, Nicolas Flammarion

We explore the possibility of exact algorithmic learning with gradient-based methods and introduce a differentiable framework capable of strong length generalization on arithmetic tasks. Our approach centers on Differentiable Finite-State Transducers (DFSTs), a Turing-complete model family that avoids the pitfalls of prior architectures by enabling constant-

Haran Mouli

In previous papers, Drungilas et al. study the problem of which triples of positive integers $(a, b, c)$ can be realized as $([E: \mathbb{Q}], [F: \mathbb{Q}], [EF: \mathbb{Q}])$, where $E$ and $F$ are number fields, using techniques from field theory. We shall study this problem rephrased in the language of groups using the Galois correspondence to simplify

Zeng Wang, Weihua Xiao, Minghao Shao, Raghu Vamshi Hemadri

Large Language Models (LLMs) show strong performance in RTL generation, but different models excel on different tasks because of architecture and training differences. Prior work mainly prompts or finetunes a single model. What remains not well studied is how to coordinate multiple different LLMs so they jointly improve RTL quality while also reducing cost,

Zeng Wang, Minghao Shao, Akashdeep Saha, Ramesh Karri

Graph neural networks (GNNs) have shown promise in hardware security by learning structural motifs from netlist graphs. However, this reliance on motifs makes GNNs vulnerable to adversarial netlist rewrites; even small-scale edits can mislead GNN predictions. Existing adversarial approaches, ranging from synthesis-recipe perturbations to gate transformations

Anthony O. Lara, Justin J. Talbot, Zhe Wang, Martin Head-Gordon

Large atomic-orbital (AO) basis sets of at least triple and preferably quadruple-zeta (QZ) size are required to adequately converge Kohn-Sham density functional theory (DFT) calculations towards the complete basis set limit. However, incrementing the cardinal number by one nearly doubles the AO basis dimension, and the computational cost scales as the cube o

Zaza N. Osmanov

The article discusses the possibility of a Type-III extraterrestrial civilization constructing megastructures around a galaxy in regions where the galactic radiation becomes indistinguishable from the CMB radiation. For a Milky Way-like galaxy, we estimated the corresponding distance from its center at which a solar mass megastructure would need to be placed

Sekoul Krastev, Hilary Sweatman, Anni Sternisko, Steve Rathje

As large language models (LLMs) rapidly displace traditional expertise, their capacity to correct misinformation has become a core concern. We investigate the idea that prompt framing systematically modulates misinformation correction - something we term 'epistemic fragility'. We manipulated prompts by open-mindedness, user intent, user role, and complexity.

Anqi Dong, Amirhossein Taghvaei, Tryphon T. Georgiou

We revisit the problem of finding the shortest path between two selected vertices of a graph and formulate this as an $\ell_1$-regularized regression -- Least Absolute Shrinkage and Selection Operator (lasso). We draw connections between a numerical implementation of this lasso-formulation, using the so-called LARS algorithm, and a more established algorithm

Rémy Rahem, Wael Suleiman

Recent progress in legged locomotion has allowed highly dynamic and parkour-like behaviors for robots, similar to their biological counterparts. Yet, these methods mostly rely on egocentric (first-person) perception, limiting their performance, especially when the viewpoint of the robot is occluded. A promising solution would be to enhance the robot's enviro

Anthony Quas

Ledrappier and Walters's article "A Relativised Variational Principle for Continuous Transformations", J. Lond. Math. Soc. (2) 16 (1977), no.3, 568-576) is a landmark in the development of Thermodynamic Formalism. This survey, aimed at newcomers to the field and experts in adjacent fields discusses the background, the Ledrappier-Walters article and some subs

Ricardo Palomino Piepenborn

It is shown that images of cross-sections of surjective morphisms $f: \Gamma \longrightarrow \Delta$ of divisible abelian $o$-groups are exactly divisible, tame (equivalently, relative Dedekind complete) and cofinal subgroups of $\Gamma$ compatible with $f$ in a suitable sense. The note concludes with an application to real closed valued fields.

Sandro Coriasco, Alexandre Kirilov, Wagner Augusto Almeida de Moraes, Pedro Meyer Tokoro

We establish necessary and sufficient conditions for the closedness of the range of a class of first-order differential operators associated with an involutive structure on $M\times\mathbb{T}^m$, where $M$ is a non-compact manifold satisfying suitable geometric assumptions and $\mathbb{T}^m$ is the $m$-dimensional torus. In addition, we prove that a weaker n

Gaurav Harsha, Selina Dirnböck, Emanuel Gull, Vojtěch Vlček

Non-trivial topological phases often emerge in narrow-gap semiconductors with a delicate blend of spin-orbit coupling and electron correlation. The diamond-lattice allotrope of Sn ($\alpha$-Sn) exemplifies this behavior, hosting multiple topological phases that can be tuned by small distortions in the lattice. Despite rapid experimental progress, theoretical

Amir Mohammad Ezzati, Alireza Malekhosseini, Armin Khosravi, Mohammad Hossein Rohban

Domain generalization is critical in computational pathology (CPath) due to inherent domain shifts caused by variations in staining protocols, scanner devices, and imaging settings across clinical centers. Vision-language models (VLMs), such as PLIP-a pathology-tuned CLIP-trained on image-text pairs across diverse domains, serve as strong knowledge distillat

Sergi Burniol Clotet

We establish a rigidity result for the unstable foliations of transitive Anosov flows on 3-manifolds: if the unstable foliations of two such flows are equivalent (that is, if there exists a homeomorphism mapping one foliation to the other), then the flows are topologically conjugate up to a constant change of time. This result partially generalizes earlier r

Salman Jan, Toqeer Ali Syed, Gohar Ali, Ali Akarma

The paper presents a detailed Agentic Artificial Intelligence (AI) model that would enable people with disabilities and neurodivergence to lead healthier lives and have more regular days. The system will use a multi-layer structure; it will include an Application and Interface Layer, an Agents Layer, and a Data Source Layer to provide adaptive, transparent,

Martin Frohn, Niels Holtgrefe, Leo van Iersel, Mark Jones

Phylogenetic trees and networks are graphs used to model evolutionary relationships, with trees representing strictly branching histories and networks allowing for events in which lineages merge, called reticulation events. While the question of data sufficiency has been studied extensively in the context of trees, it remains largely unexplored for networks.

Yajun Yu, Guoping Xu, Steve Jiang, Robert Timmerman

To develop an integrated transcriptome-proteome framework for identifying concurrent biomarkers predictive of radiation response, as measured by survival fraction at 2 Gy (SF2), in non-small cell lung cancer (NSCLC) cell lines. RNA sequencing (RNA-seq) and data-independent acquisition mass spectrometry (DIA-MS) proteomic data were collected from 73 and 46 NS

Robert I. Booth, Cole Comfort

The stabiliser fragment of quantum theory is a foundational building block for quantum error correction and the fault-tolerant compilation of quantum programs. In this article, we develop a sound, universal and complete denotational semantics for stabiliser operations which include measurement, classically-controlled Pauli operators, and affine classical ope

Eshed Gal, Moshe Eliasof, Javier Turek, Uri Ascher

Large Language Models (LLMs) are known for their expensive and time-consuming training. Thus, oftentimes, LLMs are fine-tuned to address a specific task, given the pretrained weights of a pre-trained LLM considered a foundation model. In this work, we introduce memory-efficient, reversible architectures for LLMs, inspired by symmetric and symplectic differen

G. Piccirilli, B. Bahr-Kalus, S. Camera, J. Asorey

Synergies between large-scale radio-continuum and optical/near-infrared galaxy surveys are a powerful tool for cosmology. Cross-correlating these surveys can constrain the redshift distribution of radio sources, mitigate systematic effects, and place constraints on cosmological models. We perform the first measurement of the clustering cross-spectrum between

Sophie Wright

We study random covers of a closed hyperbolic surface $\Sigma$, subject to the condition that, for $k\geq 2$, the fundamental group is isomorphic to the free group $F_k$. We show that asymptotically they distribute according to a specific probability measure on the moduli space of metric graphs. As we will demonstrate with explicit calculations for $k=2$, th

Blaise Madiega, Mathieu Olivier

Transient computational fluid dynamics (CFD) remains expensive when long horizons and multi-scale turbulence are involved. Data-driven surrogates promise relief, yet many degrade over multiple steps or drift from physical behavior. This work advances a hybrid path: an incremental time-stepping U Net LSTM model that forecasts unsteady dynamics by predicting f

Anton Bulle Labate, Valesca Moura de Sousa, Sandro Rama Fiorini, Leonardo Guerreiro Azevedo

Large Language Models (LLMs) have become increasingly capable of interacting with external tools, granting access to specialized knowledge beyond their training data - critical in dynamic, knowledge-intensive domains such as Chemistry and Materials Science. However, large tool outputs can overflow the LLMs' context window, preventing task completion. Existin

Yue Huang, Dixant B. Sapkota, Manish K. Singh

Power systems are globally experiencing an unprecedented growth in size and complexity due to the advent of nonconventional generation and consumption technologies. To navigate computational complexity, power system dynamic models are often reduced using techniques based on singular perturbation. However, several technical assumptions enabling traditional ap

Lukas Simon, Sven Yannick Klein

In these proceedings, we report on our progress in developing the $\texttt{history}$ framework, which aims to implement the fully-local Nested Soft-Collinear infrared subtraction scheme for the automated phase-space integration of color-singlet production processes in hadronic collisions at NNLO accuracy. We validate our implementation for quark-antiquark-in

Ethan Friesen, Sasha Morton-Salmon, Md Nahidul Islam Opu, Shahidul Islam

Bug prediction has long been considered the "prince" of empirical software engineering research, and accordingly, a substantial body of work has focused on predicting bugs to enable early preventive actions. However, most existing studies operate at the class or file level, which practitioners have found to be of limited practical value. As a result, method-

Tarek Al Mustafa

Knowledge Graphs (KGs) bear great potential for ecology and biodiversity researchers in their ability to support synthesis and integration efforts, meta-analyses, reasoning tasks, and overall machine interoperability of research data. However, this potential is yet to be realized as KGs are notoriously difficult to interact with via their query language SPAR

Ioannis Karageorgiou, Angelos Michaelides, Fabian Berger

Reactive dopant atoms embedded in inert host metal surfaces define the active sites in single-atom alloys (SAAs), yet SAA synthesis remains challenging. To address this, we elucidate how dopant adatoms deposited on Cu and Ag surfaces become incorporated into the metal and identify periodic trends from early to late transition metals (TMs) using density funct

Parvin Bayati, Stewart A. Mallory

The directed motion of active colloids is governed by spatial variations in surface chemistry and interfacial stress, yet these properties remain extremely difficult to measure directly. We introduce a physics-informed neural network framework that infers the slip distribution driving propulsion from partial observations of the surrounding flow. By combining

José Luis Jaramillo, Lamis Al Sheikh, Jérémy Besson, Badri Krishnan

Current state-of-the-art approaches to black hole (BH) dynamics, encompassing several effective approximation schemes, offer a remarkable control of the quantitative aspects of strong gravity. They also provide key insights into some qualitative aspects of the problem. In spite of this, there remain blind spots that hinder the understanding of the mechanisms

Yukta Pareek, Khadija Omar Said, Satadru Dey, Ashish Ranjan Kumar

Underground mining operations are actively exploring the use of large-format lithium-ion batteries (LIBs) to power their equipment. LIBs have high energy density, long cycle life, and favorable safety record. They also have low noise, heat, and emission footprints. This fosters a conducive workplace environment for underground mining personnel. However, many

Rafat Qubaja, Murray Moinester, Joel Kronfeld

Sequestration of atmospheric CO$_2$ in a Mediterranean semi-arid Aleppo Pine Forest (Pinus halepensis) close to the border of the semi-arid timberline was characterized and quantified under field conditions. Measurements of organic and inorganic CO$_2$ sequestration with gas exchange and stock counting approaches were made in both rainfed control (approximat

Katharine E. Jensen, Chelsea S. Davis

This review provides an introduction to the essential physics of soft adhesion, including the thermodynamics of adhesion and wetting, the mechanics of contact with deformable materials, and the material properties that most affect interfacial interactions with soft solid gels and elastomers. Throughout, we emphasize both foundational physics and current expe

Mirela G. Tulbure, Julio Caineta, Mark Broich, Mollie D. Gaines

Floods are among the most damaging weather-related hazards, and in 2024, the warmest year on record, extreme flood events affected communities across five continents. Earth observation (EO) satellites provide critical, frequent coverage for mapping inundation, yet operational accuracy depends heavily on labeled datasets and model generalization. Recent Geosp

Ali Fatemiabhari, Horatiu Nastase, Carlos Nunez, Dibakar Roychowdhury

We study holographic Krylov complexity in the Anabalon-Ross solitonic background, a top-down Type IIB solution describing a twisted-circle compactification of ${\cal N}=4$ SYM that flows to a confining, gapped three-dimensional theory. Following the proposal that the time derivative of Krylov complexity is dual to the proper radial momentum of a falling bulk

Niels Bassler, Giuseppe Schettino, Hugo Palmans, Thomas Friedrich

Spatially Fractionated Radiation Therapy (SFRT) produces highly heterogeneous dose distributions, for which conventional metrics such as peak, valley, or average dose can yield ambiguous or inconsistent estimates of therapeutic window changes. These dose descriptors are not uniquely linked to biological outcome, complicating comparisons between modalities. W

Alberto Compagnoni, Marco Morini, Sara Sarto, Federico Cocchi

Multimodal Large Language Models (MLLMs) have shown impressive capabilities in jointly understanding text, images, and videos, often evaluated via Visual Question Answering (VQA). However, even state-of-the-art MLLMs struggle with domain-specific or knowledge-intensive queries, where relevant information is underrepresented in pre-training data. Knowledge-ba

Damian Rovara, Lukas Burgholzer, Robert Wille

Qubit reuse offers a promising way to reduce the hardware demands of quantum circuits, but current approaches are largely restricted to reordering measurements and applying qubit resets. In this work, we present an approach to further optimize quantum circuits by fully utilizing the potential of dynamic quantum circuits-more precisely by moving measurements

Stéphane Ouvry, Alexios P. Polychronakos

We present a historical review of anyon and exclusion statistics, introduced in the 1980s and 1990s respectively, and then turn to developments in the recently introduced inclusion statistics. In contrast to exclusion statistics, where particles tend to be more exclusive than usual fermions, inclusion statistics particles tend to be more gregarious than usua

V. Vallucci-Goy, U. Lebreuilly, M. -M. Mac Low, P. Hennebelle

Context: The streaming instability (SI) is a leading candidate for reaching solid densities sufficient to trigger the gravitational collapse needed for the formation of planetesimals. However, dust growth barriers appear to impede the ability to assemble sufficiently large dust particles to trigger strong clumping, providing a serious impediment to planetesi

Mikhail Sergeev, Georgii Paradezhenko, Daniil Rabinovich, Vladimir V. Palyulin

Quantum architecture search (QAS) automates the design of parameterized quantum circuits for variational quantum algorithms. The framework finds a well-suited problem-specific structure of a variational ansatz. Among possible implementations of QAS the reinforcement learning (RL) stands out as one of the most promising. Current RL approaches are single-agent

Mohammad Saleh Torkestani, Taha Mansouri

Generative AI systems now mediate newsfeeds, search rankings, and creative content for hundreds of millions of users, positioning a handful of private firms as de-facto arbiters of truth. Drawing on a comparative-historical lens, this article juxtaposes the Galileo Affair, a touchstone of clerical knowledge control, with contemporary Big-Tech content moderat

Tianxin Wei, Xuying Ning, Xuxing Chen, Ruizhong Qiu

In web environments, user preferences are often refined progressively as users move from browsing broad categories to exploring specific items. However, existing generative recommenders overlook this natural refinement process. Generative recommendation formulates next-item prediction as autoregressive generation over tokenized user histories, where each ite

Ian Lalonde, Jeff Denis, Mathieu Lamy, Camille Martin

The ability to accomplish a sit-to-stand (STS) motion is key to increase functional mobility and reduce rehospitalization risks. While raising aid (transfer) devices and partial bodyweight support (rehabilitation) devices exist, both are unable to adjust the STS training to different mobility levels. Therefore, We have developed an STS training device that a

Boyao Zhou, Shunyuan Zheng, Zhanfeng Liao, Zihan Ma

We present Splat-SAP, a feed-forward approach to render novel views of human-centered scenes from binocular cameras with large sparsity. Gaussian Splatting has shown its promising potential in rendering tasks, but it typically necessitates per-scene optimization with dense input views. Although some recent approaches achieve feed-forward Gaussian Splatting r

Yunxin Li, Ying Zhang, Christos Masouros, Sofie Pollin

Integrated Sensing and Communications (ISAC) is emerging as a key enabler for 6G networks, with signaling design at the core of its evolution. This paper reviews the paradigm shift of ISAC signaling designs from pilot-aided sensing to data payload-based approaches, with a particular focus on how these techniques can be realized within existing 5G NR structur

Lawrence Toomey, George Hobbs, James Dempsey, Shane Majewski

Data from observations of pulsars made by Murriyang, the CSIRO Parkes 64-metre radio-telescope over the last three decades are more accessible than ever before, largely due to their storage in expansive long-term archives. Containing nearly 2 million files from more than 400 Parkes pulsar projects, CSIRO's Data Access Portal is leading the global effort in m

Gianmarco Spera, Julia M. Yeomans, Sumesh P. Thampi

To study the impact of active systems on their surroundings, we introduce a model that couples an active nematic fluid to an isotropic substrate fluid via friction. We numerically show that as the active layer develops turbulence, the substrate inherits the chaotic behaviour, exhibiting a novel form of turbulence driven by locally generated stochastic forcin

Kamer Ali Yuksel, Hassan Sawaf

Creative coding and real-time shader programming are at the forefront of interactive digital art, enabling artists, designers, and enthusiasts to produce mesmerizing, complex visual effects that respond to real-time stimuli such as sound or user interaction. However, despite the rich potential of tools like GLSL, the steep learning curve and requirement for

Abeer Matar A. Almalky, Ziyan Wang, Mohaiminul Al Nahian, Li Yang

In recent years, large language models (LLMs) have achieved remarkable advances and are increasingly deployed in critical applications across diverse domains. This growing adoption raises urgent concerns about their security and robustness. In this work, we investigate the impact of Bit Flip Attacks (BFAs) on LLMs, which exploit hardware faults to corrupt mo

Yongsheng Jia, Richard Webb

We construct a new infinite-dimensional family of homogeneous quasimorphisms on the group of Hamiltonian diffeomorphisms of the two-sphere. Moreover, for any constant $K$ less than the total area of the sphere, we produce unbounded homogeneous quasimorphisms that vanish on any map supported on some disk of area at most $K$. As an application, we prove an ana

Chancharik Mitra, Yusen Luo, Raj Saravanan, Dantong Niu

Vision-Language Action (VLAs) models promise to extend the remarkable success of vision-language models (VLMs) to robotics. Yet, unlike VLMs in the vision-language domain, VLAs for robotics require finetuning to contend with varying physical factors like robot embodiment, environment characteristics, and spatial relationships of each task. Existing fine-tuni

Juan Ignacio Alvarez-Trejos, Sergio A. Balanya, Daniel Ramos, Alicia Lozano-Diez

End-to-End Neural Diarization (EEND) systems produce frame-level probabilistic speaker activity estimates, yet since evaluation focuses primarily on Diarization Error Rate (DER), the reliability and calibration of these confidence scores have been largely neglected. When fusing multiple diarization systems, DOVER-Lap remains the only established approach, op

Andrea Conti, Yolanda Lozano, Filippos Rogdakis, Christopher Rosen

We compute the defect entanglement entropy for co-dimension two superconformal monodromy defects in well known maximally symmetric holographic theories of various dimension. In each case we explicitly relate the universal part of the defect entanglement entropy to field theory data characterising the defect conformal field theory. We provide evidence that, u

Yann Chaubet, Vincent Divol

Given $n$ i.i.d. observations, we study the problem of estimating the spectrum of weighted Laplace operators of the form $\Delta_f=\Delta + \alpha \nabla \log f\cdot \nabla$, where $f$ is a positive probability density on a known compact $d$-dimensional manifold without boundary and $\alpha\in \mathbb{R}$ is a hyperparameter. These operators arise as continu

Deressa Wodajo Deressa, Hannes Mareen, Peter Lambert, Glenn Van Wallendael

We present Generative Anchored Fields (GAF), a generative model that learns independent endpoint predictors, $J$ (noise) and $K$ (data), from any point on a linear bridge. Unlike existing approaches that use a single trajectory or score predictor, GAF is trained to recover the bridge endpoints directly via coordinate learning. The velocity field $v=K-J$ emer

David Castro-Perez, Francisco Ferreira, Sung-Shik Jongmans

Multiparty session types (MPST) provide a rigorous foundation for verifying the safety and liveness of concurrent systems. However, existing approaches often force a difficult trade-off: classical, projection-based techniques are compositional but limited in expressiveness, while more recent techniques achieve higher expressiveness by relying on non-composit

André Chailloux

In recent years, a particularly interesting line of research has focused on designing quantum algorithms for code and lattice problems inspired by Regev's reduction. The core idea is to use a decoder for a given code to find short codewords in its dual. For example, Jordan et al. demonstrated how structured codes can be used in this framework to exhibit some

Shubhankar Borse, Phuc Pham, Farzad Farhadzadeh, Seokeon Choi

Despite recent advances in personalized image generation, existing models consistently fail to produce reliable multi-human scenes, often merging or losing facial identity. We present Ar2Can, a novel two-stage framework that disentangles spatial planning from identity rendering for multi-human generation. The Architect predicts structured layouts, specifying

Gui-Jun Ding, Ranjeet Kumar, Newton Nath, Rahul Srivastava

The leptonic mixing matrix is examined within bi-large mixing patterns and confronted with the latest results announced by the Jiangmen Underground Neutrino Observatory (JUNO). We analyze the viability of bi large mixing schemes and assess JUNO's ability to test neutrino mixing and discriminate among different bi-large mixing patterns, some of which are stro

Amirmojtaba Sabour, Michael S. Albergo, Carles Domingo-Enrich, Nicholas M. Boffi

A common recipe to improve diffusion models at test-time so that samples score highly against a user-specified reward is to introduce the gradient of the reward into the dynamics of the diffusion itself. This procedure is often ill posed, as user-specified rewards are usually only well defined on the data distribution at the end of generation. While common w

Jiatong Shi, Haoran Wang, William Chen, Chenda Li

Neural speech codecs have achieved strong performance in low-bitrate compression, but residual vector quantization (RVQ) often suffers from unstable training and ineffective decomposition, limiting reconstruction quality and efficiency. We propose PURE Codec (Progressive Unfolding of Residual Entropy), a novel framework that guides multi-stage quantization u

Yiwen Zhang, Joseph Tung, Ruojin Cai, David Fouhey

3D foundation models (3DFMs) have recently transformed 3D vision, enabling joint prediction of depths, poses, and point maps directly from images. Yet their ability to reason under extreme, non-overlapping views remains largely unexplored. In this work, we study their internal representations and find that 3DFMs exhibit an emergent understanding of extreme-v

Haoyi Wang, Licheng Luo, Yiannis Kantaros, Bruno Sinopoli

Multi-robot navigation in cluttered environments presents fundamental challenges in balancing reactive collision avoidance with long-range goal achievement. When navigating through narrow passages or confined spaces, deadlocks frequently emerge that prevent agents from reaching their destinations, particularly when Reinforcement Learning (RL) control policie

Moritz Hauck, Alexei Lozinski

In this paper, we propose a high-order extension of the multiscale method introduced by the authors in [SIAM J. Numer. Anal., 63(4) (2025), pp. 1617--1641] for heterogeneous Stokes problems, while also providing several other improvements, including a better localization strategy and a more precise pressure reconstruction. The proposed method is based on the

Yueran Zhao, Chang-Sheng Mei, Nathan J. McDannold, Shenyan Zong

Background: Accurate proton resonance frequency (PRF) MR thermometry is essential for monitoring temperature rise during thermal ablation with high intensity focused ultrasound (FUS). Conventional referenceless methods such as complex field estimation (CFE) and phase finite difference (PFD) tend to exhibit errors when susceptibility-induced phase discontinui

Amirreza Zamani, Ayfer Özgür, Mikael Skoglund

In this paper, we study an information-theoretic problem of designing a fair representation under a bounded point-wise statistical (demographic) parity constraint. More specifically, an agent uses some useful data (database) $X$ to solve a task $T$. Since both $X$ and $T$ are correlated with some latent sensitive attribute or secret $S$, the agent designs a

Himani Verma, Kamal Singh, Ranjan K. Mallik

Coherent free-space optical (FSO) communication is recognized as a key enabler for ultra-high-capacity fronthaul and backhaul links in next-generation wireless networks. Spectrally efficient $M$-ary quadrature amplitude modulation (MQAM) formats are well-suited for these links. However, theoretical analyses of adaptive MQAM transmissions over terrestrial FSO

Muhammad Shahzaib

The electron flux diverts electrons from optimal hydrogen production pathways to competitive pathways, which overall reduces the efficiency of the photo fermentation hydrogen production (PFHP) system. For tackling electron flux and metabolic pathway regulation, a hybrid material (nano-capattery (NC)) was developed based on cobalt-iron-nitrogen doped biochar

Mohaiminul Al Nahian, Abeer Matar A. Almalky, Gamana Aragonda, Ranyang Zhou

The rapid advancement of large language models (LLMs) has sparked growing interest in understanding their security vulnerabilities, particularly Trojan attacks that enable stealthy manipulation of model behavior. Traditional Trojan methods typically alter inputs and/or model weights, relying on white-box assumptions that require access to data or model inter

Oscar Montiel Ross

This paper develops the foundations of Quantum Granular Computing (QGC), extending classical granular computing including fuzzy, rough, and shadowed granules to the quantum regime. Quantum granules are modeled as effects on a finite dimensional Hilbert space, so granular memberships are given by Born probabilities. This operator theoretic viewpoint provides

Anna Hasenfratz, Oliver Witzel

In recent years tantalizing signs for a novel phase have been reported that is chirally symmetric but nevertheless exhibits massive bound states. The necessary condition for such a phase, referred to as Symmetric Mass Generation (SMG), is the cancellation of all (continuous and discrete) 't~Hooft anomalies. In 3+1 dimensions this occurs in systems containing

Dongyang Liu, Peng Gao, David Liu, Ruoyi Du

Diffusion model distillation has emerged as a powerful technique for creating efficient few-step and single-step generators. Among these, Distribution Matching Distillation (DMD) and its variants stand out for their impressive performance, which is widely attributed to their core mechanism of matching the student's output distribution to that of a pre-traine

Boyang Gu, Adrian Diaz, Yang Li, Youping Chen

This work presents a finite element method for simulating dynamic processes that involve the coupled evolution of dislocation motion and crack propagation. The method numerically solves the Concurrent Atomistic-Continuum (CAC) formulation of the conservation of linear momentum. A crystalline material is discretized at the unit-cell level using 6-node prism e

Andrea Cianchi, Flavia Giannetti, Antonia Passarelli di Napoli, Christoph Scheven

This work concerns stationary Stokes type systems governed by a general class of non-necessarily power-type nonlinearities. Fractional regularity properties of the symmetric gradient of local solutions are established, depending on a balance between the nonlinearity of the differential operator and the degree of integrability of the datum on right-hand side.

Morad Laglil, Emilie Devijver, Eric Gaussier, Bertrand Pracca

Inspired by recent advances in large language models, foundation models have been developed for zero-shot time series forecasting, enabling prediction on datasets unseen during pretraining. These large-scale models, trained on vast collections of time series, learn generalizable representations for both point and probabilistic forecasting, reducing the need

Arasti Afrasiabi, Farough Rahimzadeh, Alireza Keshavarzi

This review paper explores the state-of-the-art in non-intrusive methods for detecting and characterising buried infrastructure, focusing on Electrical Resistivity Tomography (ERT), Infrared Thermography (IRT), and magnetometry, along with data fusion techniques and mathematical estimators. ERT and IRT offer distinct advantages in subsurface imaging, while m

Richard J. Szabo

These are expanded lecture notes of a mini-course whose objectives were to introduce the basic concepts, constructions and techniques of noncommutative geometry, as well as their uses as a framework for modelling quantum spacetime. Key mathematical approaches presented include operator algebras such as $C^*$-algebras, K-theory, spectral geometry, quantum gro

Rajdeep Mondal, Abhishake Sadhukhan

We study linear scalar perturbations of the four-dimensional, traversable wormhole solution of Maldacena, Milekhin, and Popov(arXiv:1807.04726). The geometry is constructed by matching an asymptotically flat, near-extremal Reissner--Nordstr\"om region to a throat described by $AdS_2 \times S^2$, supported by charged massless fermions. We derive the effective

Xin Sheng, Bennett Link, Matthew E. Caplan, Yuri Levin

We study the superfluid vortex motion in the neutron star inner crust through direct three-dimensional simulations of the coupled dynamics of the vortex and the nuclear lattice. We demonstrate the pinning of an initially moving vortex to the lattice through excitation of lattice vibrations, and show that the efficiency of this process is higher for attractiv

Aditya Pratap Singh, Shrey Shah, Ramanakumar Sankar, Emma Dahl

Insights into Jupiter's atmospheric dynamics are vital for understanding planetary meteorology and exoplanetary gas giant atmospheres. To study these dynamics, we require high-resolution, photometrically calibrated observations. Over the last 9 years, the Juno spacecraft's optical camera, JunoCam, has generated a unique dataset with high spatial resolution,

A. Afifi, A. Kalimullin, S. Korchagin, I. Kudryashov

This study explores the use of deep learning for the authentication and attribution of paintings, focusing on the complex case of Peter Paul Rubens and his workshop. A convolutional neural network was trained on a curated dataset of verified and comparative artworks to identify micro-level stylistic features characteristic of the master s hand. The model ach

Silin Cheng, Kai Han

Vision-language models (VLMs), such as CLIP, have shown strong generalization under zero-shot settings, yet adapting them to downstream tasks with limited supervision remains a significant challenge. Existing multi-modal prompt learning methods typically rely on fixed, shared prompts and deterministic parameters, which limits their ability to capture instanc

Dian Zheng, Manyuan Zhang, Hongyu Li, Kai Zou

Unified multimodal models for image generation and understanding represent a significant step toward AGI and have attracted widespread attention from researchers. The main challenge of this task lies in the difficulty in establishing an optimal training paradigm due to inherent conflicting targets in understanding and generation tasks. To alleviate these con