March 2026 arXiv papers — page 5

Xinhong Dai, Yuan Gao, Hao Jiang, Xiaojun Yuan

This paper studies an unmanned aerial vehicle (UAV) position and attitude sensing problem, where a base station equipped with an antenna array transmits signals to a predetermined potential flight region of a flying UAV, and exploits the reflected echoes for wireless imaging. The UAV is represented by an electromagnetic point cloud in this region that contai

Fumitatsu Iwase

This study investigates the spatial confinement of topological $\pi$-modes in one-dimensional chiral-symmetric systems. In conventional periodic and quasiperiodic structures, edge-mode wave functions inevitably penetrate the bulk. To suppress this, inverse design of a potential sequence is performed using a generative model under a global topological constra

Mingyeong Song, Seoyeon Ko, Junhyug Noh

Binaural audio delivers spatial cues essential for immersion, yet most consumer videos are monaural due to capture constraints. We introduce SIREN, a visually guided mono to binaural framework that explicitly predicts left and right channels. A ViT-based encoder learns dual-head self-attention to produce a shared scene map and end-to-end L/R attention, repla

Michael Kifer, Theresa Swift

ErgoAI is a high level, multi-paradigm logic programming language and system developed by Coherent Knowledge Systems as an enhancement of and a successor to the popular Flora-2 system. ErgoAI is oriented towards scalable knowledge representation and reasoning, and can exploit both structured knowledge as well as knowledge derived from external sources such a

Brahim Erraji, Michaël Perrot, Aurélien Bellet

While clients may join federated learning to improve performance on data they rarely observe locally, they often remain self-interested, expecting the global model to perform well on their own data. This motivates an objective that ensures all clients achieve a similar loss gap -the difference in performance between the global model and the best model they c

Francesca Waines, Angharad Weeks, Vincent Van Eylen

Precise homogeneous stellar characterisation is crucial for our understanding of the physical properties of exoplanets, their demographics and the environment from which they are formed. We present a homogeneous catalogue of 717,807 TESS FGK dwarfs and early subgiants, making use of isochrones along with Gaia DR3 inputs of photometry, parallax and spectrosco

Thomas Blom, Félix Loubaton, Jaco Ruit

We characterize the exponentiable objects for a wide range of structures prevalent in $\infty$-categorical algebra, extending the construction of Day convolution to more general structures than $\infty$-operads. More precisely, we give a criterion that is both necessary and sufficient for many of these structures encountered in practice, such as (equivariant

Thomas Sayer, Ethan H. Fink, Zachary R. Wiethorn, Devin R. Williams

Ultrafast multidimensional spectroscopies are powerful tools that can access charge and energy flow in complex materials, shifting chemical kinetics, and even many-body interactions in correlated matter. However, current implementations typically involve complex apparatuses and long averaging times. As a result, these methods have been limited to detailed me

Yan Lin, Jonas A. Finkler, Tao Du, Jilin Hu

Amorphous materials are solids that lack long-range atomic order but possess complex short- and medium-range order. Unlike crystalline materials that can be described by unit cells containing few up to hundreds of atoms, amorphous materials require larger simulation cells with at least hundreds or often thousands of atoms. Inverse design of amorphous materia

Douglas F. Copatti, Giuliano G. La Guardia, Waldir S. Soares, Edson D. Carvalho

In this paper, we construct several new quantum Floquet codes on compact, orientable, as well as non-orientable surfaces. In order to obtain such codes, we identify these surfaces with hyperbolic polygons and examine hyperbolic semi-regular tessellations on such surfaces. The method of construction presented here generalizes similar constructions concerning

Rada Ziganshina

In this paper, we establish an $\varepsilon$-regularity theorem for minimizers of an Alt-Phillips type functional subject to constraint maps. We prove that under sufficiently small energy, the minimizers exhibit regularity, and hence proving the smoothness of these maps. From here, we bootstrap to optimal regularity.

Andreas Bluhm, Matthias C. Caro, Francisco Escudero Gutiérrez, Junseo Lee

In this work, we study the problems of certifying and learning quantum $k$-local Hamiltonians, for a constant $k$. Our main contributions are as follows: - Certification of Hamiltonians. We show that certifying a local Hamiltonian in normalized Frobenius norm via access to its time-evolution operator can be achieved with only $O(1/\varepsilon)$ evolution tim

Mustafa Mete, Anastasia Bolotnikova, Alexander Schuessler, Jamie Paik

Wearable robots aim to seamlessly adapt to humans and their environment with personalized interactions. Existing supernumerary robotic limbs (SRLs), which enhance the physical capabilities of humans with additional extremities, have thus far been developed primarily for task-specific applications in structured industrial settings, limiting their adaptability

Silvia Bertoluzza

We present BioNetFlux, an open-source Python framework for the numerical simulation of coupled systems of partial differential equations (PDEs) on one-dimensional multi-arc networks by the Hybridized Discontinuous Galerkin method. Its design targets biological transport phenomena on graph-like geometries that arise naturally in microfluidic organ-on-chip (Oo

Roberto Balossi, Paolo Tanga, Aldo Dell'Oro

The dynamical and physical properties of asteroid family members are widely used to reconstruct the collisional evolution of the main belt and of individual objects. Families offer insights into the properties of the parent bodies and the fragmentation processes responsible for their formation. We investigate a poorly constrained phase of early collisional e

Xiao Qian, Shangjia Dong

Accurate prediction of evacuation behavior is critical for disaster preparedness, yet models trained in one region often fail elsewhere. Using a multi-state hurricane evacuation survey, we show this failure goes beyond feature distribution shift: households with similar characteristics follow systematically different decision patterns across states. As a res

Max Hennick, Guillaume Corlouer

A key problem in the modern study of AI is predicting and understanding emergent capabilities in models during training. Inspired by methods for studying reactions in quantum chemistry, we present the ``2-datapoint reduced density matrix". We show that this object provides a computationally efficient, unified observable of phase transitions during training.

Emanuele Panizon

We analytically solve the finite-time control problem of driving an overdamped particle via an optical trap under costly measurement. By formulating this mesoscopic information engine within the Partially Observable Markov Decision Process (POMDP) framework, we demonstrate that the underlying Linear-Quadratic-Gaussian (LQG) dynamics decouple the optimal meas

Leonardo Colò, David Kohel

We study the modular curves defined by Weber functions, and associated modular polynomials, action of $\mathrm{SL}_2(\mathbb{Z})$, and parametrizations of elliptic curves with a view to the study of the isogeny graphs that they determine, particularly for supersingular elliptic curves. In addition to applications to efficient isogeny computation in cryptogra

Cristian Santini, Sebastian Barzaghi, Paolo Sernani, Emanuele Frontoni

This paper introduces ENEIDE (Extracting Named Entities from Italian Digital Editions), a silver standard dataset for Named Entity Recognition and Linking (NERL) in historical Italian texts. The corpus comprises 2,111 documents with over 8,000 entity annotations semi-automatically extracted from two scholarly digital editions: Digital Zibaldone, the philosop

Xaver Fabian, Marco Guarnieri, Boris Köpf, Jose F. Morales

Speculative execution enhances processor performance by predicting intermediate results and executing instructions based on these predictions. However, incorrect predictions can lead to security vulnerabilities, as speculative instructions leave traces in microarchitectural components that attackers can exploit. This is demonstrated by the family of Spectre

Zhigang Wu, Weike Wang, Yinghui Zhang

The generalized Huygens principle for the Cauchy problem of a generic non-conservative compressible two-fluid model in R3 was established. This work fills a key gap in the theory, as previous results were confined to systems with full conservation laws or ``equivalent" conservative structures from specific compensatory cancellations in Green's function. Inde

Léopold Maillard, Francis Engelmann, Tom Durand, Boxiao Pan

Embodied AI depends on interactive 3D environments that support meaningful activities for diverse users, yet assessing their functional affordances remains a core challenge. We introduce SceneTeract, a framework that verifies 3D scene functionality under agent-specific constraints. Our core contribution is a grounded verification engine that couples high-lev

E. H. Krenkel, M. A. Tanatar, E. I. Timmons, S. L. Bud'ko

The mechanism of anomalous superlinear temperature-dependent resistivity, $\rho (T)$, in the metallic unconventional clathrate BaNi$_2$P$_4$ was studied by examining its evolution with artificial disorder induced by low-temperature ($\sim$ 20 K) 2.5 MeV electron irradiation. We find a dominant effect of the tetragonal-orthorhombic transition at $T_s$ ($ \sim

Can Zheng, Jiguang He, Guofa Cai, Nannan Li

Future wireless systems increasingly require predictive and transferable representations that can support multiple physical-layer (PHY) tasks under dynamic environments. However, most existing supervised learning-based methods are designed for a single task, which leads to high adaptation cost. To address this issue, we propose a joint-embedding predictive a

Roberto Ladu, Simone Tagliente

We show that any topological, closed, oriented, non-spin $4$-manifold with fundamental group $\mathbb{Z}_{4k}$ and $\min(b_2^+, b_2^-)\geq 15$, has either none or infinitely many distinct smooth structures. Furthermore, we construct infinitely many non-diffeomorphic, irreducible, smooth structures on manifolds with signature zero, $b_2^+$ even and fundamenta

Franco Rugolon, Korbinian Randl, Braslav Jovanovic, Ioanna Miliou

Multimodal Machine Learning offers a holistic view of a patient's status, integrating structured and unstructured data from electronic health records (EHR). We propose a framework to predict metastasis risk one month prior to diagnosis, using six months of clinical history from EHR data. Data from four cancer cohorts collected at Karolinska University Hospit

Tim R. Davidson, Benoit Seguin, Enrico Bacis, Cesar Ilharco

Although many AI applications of interest require specialized multi-modal models, relevant data to train such models is inherently scarce or inaccessible. Filling these gaps with human annotators is prohibitively expensive, error-prone, and time-consuming, leading model builders to increasingly consider synthetic data as a scalable alternative. However, exis

Nayun Jia, Chen Zhang, Xin Zhang

Light primordial black holes (PBHs) that fully evaporate before Big Bang Nucleosynthesis (BBN) produce dark radiation (DR) via Hawking radiation of gravitons, contributing to the effective number of relativistic species $\Delta N_{\rm eff}$. If the particle spectrum contains a beyond-the-Standard-Model (BSM) boson with Compton wavelength comparable to the bl

Leonardo Colò

We propose a bridge between oriented supersingular elliptic curves and the arithmetic of modular curves. To an $\mathcal{O}$-oriented supersingular curve, we attach a class in the relative homology group $H(X_0(N),C,\mathbb{Z})$, i.e. modular symbols, compatible with the Hecke action. We then compute vectors of $\ell$-adic periods by pairing with weight $2$

Abderrezzaq Sendjasni, Mohamed-Chaker Larabi

The rapid evolution of Generative AI (GenAI) models has led to synthetic images of unprecedented realism, challenging traditional methods for distinguishing them from natural photographs. While existing detectors often rely on single-feature spaces, such as statistical regularities, semantic embeddings, or texture patterns, these approaches tend to lack robu

Anthony Massidda

The study of Feynman integrals through the lens of intersection theory offers a unifying framework for their analysis, capturing both the linear and quadratic relations that arise among integrals. In doing so, it provides a powerful method for systematically reducing them to the so called master integrals, a necessary strategy for multiloop contributions, wh

Grant Molnar

For events $A$ and $B$, we have \[ \mathbb{P}(A\mid B) > \mathbb{P}(A\mid \neg B) \qquad\Longleftrightarrow\qquad \mathbb{P}(B\mid A) > \mathbb{P}(B\mid \neg A) \] whenever all four quantities are defined. In other words, $B$ is evidence for $A$ if and only if $A$ is evidence for $B$. This note gives seven different proofs of this fact -- by cross-multiplica

Lorenzo Asprea, Francesco Sarandrea, Alessio Romano, Jacob Lange

We present Q-Transform Amplitude Modulation (QTAM), a novel, fully invertible implementation of the Constant-Q Transform algorithm, designed to enable robust signal denoising and the disentanglement of overlapping transient events in current and next generation gravitational wave (GW) observatories. Time-frequency (TF) analysis faces a fundamental dichotomy:

Boshko Koloski, Marjan Stoimchev, Jurica Levatić, Dragi Kocev

Hierarchical multi-label classification (HMLC) is essential for modeling structured label dependencies in remote sensing. Yet existing approaches struggle in multi-path settings, where images may activate multiple taxonomic branches, leading to underuse of hierarchical information. We propose MAPLE (Multi-Path Adaptive Propagation with Level-Aware Embeddings

Xin Li, Qingyuan Qi, Kemi Ding

We address a noncooperative game problem in multi-controller system under delayed and asymmetric information structure. Under these conditions, the classical separation principle fails as estimation and control design become strongly coupled, complicating the derivation of an explicit Nash equilibrium. To resolve this, we employ a common-private information

Yuan Liao, Federico Delussu, Sílvia de Sojo, Laura Alessandretti

Urban deprivation is traditionally measured using static, residence-based indicators, capturing the socioeconomic, demographic, and spatial conditions of neighborhoods. However, this approach overlooks how daily movement allows residents to navigate the city, potentially exposing them to opportunities that differ significantly from their residential environm

Dibya Chakraborty, Rudnei O. Ramos

We investigate brane inflation, focusing on warm inflation realizations within a warped throat geometry. While the standard scenario relies on a single mobile $D3$-brane moving radially toward an anti-$D3$-brane at the tip of the throat, we propose two distinct inflationary pictures. In our approach, the radial and angular coordinates of a $D3$-brane on a wa

J. McDonald

We compare conventional non-minimally coupled Peccei-Quinn (PQ) inflation with a version of the model in which unitarity conservation is imposed by additional Jordan frame interactions. Assuming instantaneous reheating, the unitarity-conserving model is within 1$\sigma$ agreement with the central value of the scalar spectral index reported by the ACT collabo

Shaina Gandhi, Gourab Paul, Srijata Lahiri, Saurabh Basu

We investigate the flat-band properties and topological phase transitions in a non-Hermitian twisted bilayer $\alpha-T_3$ lattice. Here, non-Hermiticity is introduced via Hatano-Nelson-type asymmetric hopping, while an aligned hexagonal boron nitride substrate provides a staggered sublattice mass to the system. We find that the introduction of non-reciprocal

Radu Nicolae, Dante Niewenhuis, Sacheendra Talluri, Alexandru Iosup

Datacenters are vital to our digital society, but consume a considerable fraction of global electricity and demand is projected to increase. To improve their sustainability and performance, we envision that simulators will become primary decision-making tools. However, and unlike other fields focusing on key societal infrastructure such as waterworks and mas

Syful Islam, Stefano Zacchiroli

Corrective maintenance is crucial to ensure the quality of software, thereby improving reliability and user experience. In a version control system (VCS), developers write commit messages to document their changes and support later maintenance. Still, to this day, no secondary study has mapped the research landscape of how commit messages have been used in c

Ganen Sethupathy, Lalit Dumka, Jan Schagen

Public spaces such as transport hubs, city centres, and event venues require timely and reliable detection of potentially violent behaviour to support public safety. While automated video analysis has made significant progress, practical deployment remains constrained by latency, privacy, and resource limitations, particularly under edge-computing conditions

Amir Rahmani, Maciej Dems, Michał Matuszewski

Slow light is a regime of reduced group velocity, resulting in increased photon density in optical pulses and enhanced nonlinear effects. Here, we propose the realization of slow light in the regime of strong light-matter interaction between waveguide photons and semiconductor excitons. We design a dielectric superlattice structure with a nearly-flat band ch

Alessandro Conigli, Dalibor Djukanovic, Georg von Hippel, Simon Kuberski

We present an updated determination of the hadronic vacuum polarization contribution to the running of the electromagnetic coupling $\Delta\alpha_{\mathrm{had}}^{(5)}(-Q^2)$, and of the electroweak mixing angle in the space-like momentum range up to $12 \ \mathrm{GeV}^2$. Using $N_f=2+1$ CLS ensembles at five values of the lattice spacing and several pion ma

Shanxian Lin, Yuichi Nagata, Haichuan Yang

Metaheuristic algorithms such as Particle Swarm Optimization (PSO) and Evolutionary Algorithms (EA) excel at exploring solution spaces but lack mechanisms to accumulate and reuse procedural knowledge from successful search trajectories. This paper proposes Associative Constructive Evolution (ACE), a framework that enhances metaheuristics through learned gene

Fengyang Xiao, Peng Hu, Lei Xu, XingE Guo

Real-world image restoration aims to restore high-quality (HQ) images from degraded low-quality (LQ) inputs captured under uncontrolled conditions. Existing methods typically depend on ground-truth (GT) supervision, assuming that GT provides perfect reference quality. However, GT can still contain images with inconsistent perceptual fidelity, causing models

Tae Hyun Jung, Seyong Kim, Jong-Wan Lee, Chang Sub Shin

We revisit QCD-induced little inflation in which the Universe begins with a large baryon chemical potential and undergoes a strong first-order QCD phase transition, generating an observable stochastic gravitational-wave background in the nano-Hz range relevant for pulsar timing array (PTA) observations. We point out that the conventional homogeneous transiti

Yuan-Sen Ting

We present the Severed Floor, a framework for Macrodata Refinement of the daily astro-ph arXiv feed, deployed at Phermon Industries (formerly McPherson Laboratory, The Ohio State University). In this framework, researchers undergo a "severance procedure" that produces a digital work-self -- an innie -- while the original researcher, the outie, is free to att

Huisheng Xu, Luojia Wang, Luqi Yuan, Liang Jin

High-order perfect absorption of coherent input has recently attracted significant attention due to its broadband absorption capacity. However, the realization of a high-order perfect absorber relies on the exceptional point (EP) to coalesce the scattering zeros. Here, we present a general scattering framework and achieve the high-order perfect absorber in t

Behnam Esmayli, Pekka Koskela, Khanh Nguyen

A homemorphism between domains in $\mathbb R^n$, $n\ge 2$ is quasiconformal, with its intricate analytic and geometric consequences, if the (pointwise) linear dilatation -- a purely metric quantity -- is uniformly bounded. Gehring proved that it will suffice to verify the uniform bound up to a set of measure zero as long as we can show that the dilatation is

Jayanth Mouli, David Anderson, Antonio Cicone

Many real-life signals, such as gravitational wave measurements, biomedical signals, or geophysical data, are strongly non-stationary but can be decomposed into mono-component signals that contain only one active frequency over time. This is made possible thanks to decomposition methods developed in recent years that can handle non-stationary signals. The pr

Jing-Xiao Liao, Haoran Wang, Tao Li, Daoming Lyu

With the development of foundational models, model compression has become a critical requirement. Various model compression approaches have been proposed such as low-rank decomposition, pruning, quantization, ergodic dynamic systems, and knowledge distillation, which are based on different heuristics. To elevate the field from fragmentation to a principled d

Haofan Dong, Ozgur B. Akan

RF fingerprinting authenticates satellite transmitters by exploiting hardware-specific signal impairments, yet existing methods operate without theoretical performance guarantees. We derive the Fisher information matrix (FIM) for joint estimation of in-phase/quadrature (IQ) imbalance and power amplifier (PA) nonlinearity parameters, establishing Cram\'{e}r-R

Eros Fanì, Oğuzhan Ersoy

Large Language Models (LLMs) have achieved remarkable performance on a wide range of specialized tasks, exhibiting strong problem-solving capabilities. However, training these models is prohibitively expensive, and they often lack domain-specific expertise because they rely on general knowledge datasets. Expertise finetuning can address this issue; however,

Ping Zhao, Huifang Ma, Long Feng

We consider testing zero pricing errors in high-dimensional linear factor pricing models. Existing methods are mainly based on either an $L_2$ statistic, which is effective under dense alternatives, or an $L_\infty$ statistic, which is powerful under very sparse alternatives. To bridge these two regimes, we develop a class of $L_q$-based tests for finite $q$

Philip Z. Maymin

We derive the stochastic price process for tokens whose sole price discovery mechanism is a constant-product automated market maker (AMM). When the net flow into the pool follows a diffusion, the token price follows a constant elasticity of variance (CEV) process, nesting Black-Scholes as the limiting case of infinite liquidity. We obtain closed-form Europea

Chandler C. Payne, Kai A. Hackney, Lucas Guarenti Zangari, Sterling R. Kalogeras

Academic dishonesty has long been a concern in computing education, and the rapid growth of online learning and generative artificial intelligence (AI) has further complicated how cheating is perceived and addressed. We report on a study examining how different actors in the computer science (CS) classroom interpret potential cheating scenarios and the motiv

Quanhao Li, Wei Jiang

A human-like chess engine should mimic the style, errors, and consistency of a strong human player rather than maximize playing strength. We show that training from move sequences alone forces a model to learn two capabilities: state tracking, which reconstructs the board from move history, and decision quality, which selects good moves from that reconstruct

T. Vasina, M. Pfeiffer, R. Borth, M. Nicklas

We report a quantitative calorimetric study of UTe2 under pressure with a direct measurement of the Sommerfeld gamma coefficient, showing a three-fold enhancement of electronic effective mass when approaching the critical pressure where superconductivity is suppressed and ordered states occur. We analyse the evolution of gamma with the amplitude of the jumps

E. A. Kuznetsov

The results of recent experiments [1] on observing soliton lattices and their dislocations in vertical cylindrical channels filled with immiscible fluids with strongly different viscosities and but slightly different densities are discussed. The less viscous, lower-density fluid fills the central region of the cylinder. Injecting a light fluid from below gen

Ofelia Durante, Roberta Citro, Elia Strambini, Valeria Demontis

We report Barkhausen-like switching at millitesla fields in an $n$-doped InAs/Al nanowire Josephson junction, which serves as an interferometric probe of intrinsic magnetic reconfigurations, as evidenced by discrete switching-current jumps. At $T=30$~mK the device displays a Fraunhofer-like modulation with $I_{\mathrm{sw}}(0)\approx 0.24~\mu\mathrm{A}$ and a

Prasanjit Dey, Soumyabrata Dev, Bianca Schoen-Phelan

We address the challenge of adapting pre-trained Large Language Models (LLMs) for multivariate time-series analysis, where their deployment is often hindered by prohibitive computational and memory demands. Our solution, One-for-All, introduces Gaussian Rank-Stabilized Low-Rank Adapters (rsLoRA) to enable parameter-efficient fine-tuning of frozen LLMs. While

Arsenios Scrivens

Can classifier-based safety gates maintain reliable oversight as AI systems improve over hundreds of iterations? We provide comprehensive empirical evidence that they cannot. On a self-improving neural controller (d=240), eighteen classifier configurations -- spanning MLPs, SVMs, random forests, k-NN, Bayesian classifiers, and deep networks -- all fail the d

Chathurangi Shyalika, Utkarshani Jaimini, Cory Henson, Amit Sheth

Modern manufacturing environments demand real-time, trustworthy, and interpretable root-cause insights to sustain productivity and quality. Traditional analytics pipelines often treat anomaly detection, causal inference, and root-cause analysis as isolated stages, limiting scalability and explainability. In this work, we present CausalPulse, an industry-grad

Junran Kong, Yuwei Lu, Huan Liu, Liwei Duan

Nonequilibrium energy transport serves as one of fundamental problems in quantum thermodynamics and quantum technologies. Driven quantum master equation in the dressed picture provides an efficient way of investigating nonequilibrium energy flow in general driven-dissipative quantum systems, where the systems are simultaneously driven by the finite thermodyn

Daniel C. Qi, Kenshiro Oguri

This paper addresses the problem of steering a state distribution over a finite horizon in discrete time with output feedback. The incorporation of output feedback introduces additional challenges arising from the statistical coupling between the true state distribution and the corresponding filtered state distribution. In particular, this paper extends exis

Haris Gacanin

This vision paper addresses the research challenges of integrating traditional signal processing with Artificial Intelligence (AI) to enable energy-efficient, programmable, and scalable wireless connectivity infrastructures. While prior studies have primarily focused on high-level concepts, such as the potential role of Large Language Model (LLM) in 6G syste

Philip Z. Maymin

I derive a size premium from the constant-product automated market maker used to price Bittensor subnet tokens and test the prediction using daily data on 128 subnets. A small-minus-big factor earns 1.01% daily (Newey-West t = 3.28). The December 2025 halving of token emissions, which the theory predicts should halve the premium, reduces it from 1.17% to 0.5

Claudius Pott, Luca Wilke, Jan Wichelmann, Thomas Eisenbarth

Trusted Execution Environments (TEEs) allow the secure execution of code on remote systems without the need to trust their operators. They use static attestation as a central mechanism for establishing trust, allowing remote parties to verify that their code is executed unmodified in an isolated environment. However, this form of attestation does not cover r

The XRISM collaboration

The metal abundances in galactic nuclei carry key information on the history of star formation and mass transfer in central regions of galaxies. X-ray fluorescence analysis is a unique tool to reliably measure the abundances of various elements via simple physics. Here we present a new observation of the active nucleus in the Circinus Galaxy with the XRISM s

Clifford Bergman, Tomasz Penza, Anna B. Romanowska

The Mal'tsev product of two varieties of similar algebras is always a quasivariety. We consider the question of when this quasivariety is a variety. The main result asserts that if $\mathcal{V}$ is a strongly irregular variety with no nullary operations and at least one non-unary operation, and $\mathcal{S}$ is the variety, of the same type as $\mathcal{V}$,

Nikita M. Shirokov

We present 3D numerical-relativity evolutions of the unstable Ellis-Bronnikov wormhole using GRTeclyn, starting from exact isotropic initial data for the coupled Einstein-phantom-scalar system. With a flat initial lapse (alpha=1) and full phantom support, truncation-level noise eventually drives the rarefactive instability and rapid throat expansion. To forc

Zaid A. Abod, Furqan Aziz

Complete and high-quality multi-modal Magnetic Resonance Imaging (MRI) is essential for accurate neuro-oncological assessment, as each contrast provides complementary anatomical and pathological information. However, acquiring all modalities (e.g., T1c, T1n, T2w, T2f) for every patient is often impractical due to prolonged scan times, cost, and patient disco

Andruid Kerne

We develop a conceptualization of ideology, in which a system of ideas represents social, economic, and political relationships. We use ideology as a lens for understanding and critiquing intersecting social, economic, and political aspects of how 'AI' technologies are being developed. We observe ideological shifts. We question that the present tangling of c

Hendrik Vater, Oliver Schweins, Lukas Hölsch, Wilhelm Kirchgässner

Driven by the MagNet Challenge 2025 (MC2), increased research interest is directed towards modeling transient magnetic fields within ferrite material. An accurate time-resolved and temperature-aware H-field prediction is essential for optimizing magnetic components in applications with quasi-stationary / non-stationary excitation waveforms. Within the scope

Yahia Salaheldin Shaaban, Abdelrahman Sayed Sayed, M. Umar B. Niazi, Karl Henrik Johansson

Kazantzis-Kravaris/Luenberger (KKL) observers are a class of state observers for nonlinear systems that rely on an injective map to transform the nonlinear dynamics into a stable quasi-linear latent space, from where the state estimate is obtained in the original coordinates via a left inverse of the transformation map. Current learning-based methods for the

Michael Radica

Whether there is a cosmic shoreline that divides terrestrial planets which have atmospheres from those that don't is one of the biggest open questions in exoplanet science. Most atmosphere searches have focused on terrestrial planets around M dwarf stars, since their smaller radii compared to sun-like stars boost planet atmosphere signals. However, the highe

Rui Bao, Zheng Gao, Xiaoyu Li, Xiaoyan Feng

Diffusion-based watermarking methods embed verifiable marks by manipulating the initial noise or the reverse diffusion trajectory. However, these methods share a critical assumption: verification can succeed only if the diffusion trajectory can be faithfully reconstructed. This reliance on trajectory recovery constitutes a fundamental and exploitable vulnera

Edoardo Allegrini, Edoardo Di Paolo, Angelo Spognardi, Marinella Petrocchi

BotVerse is a scalable, event-driven framework for high-fidelity social simulation using LLM-based agents. It addresses the ethical risks of studying autonomous agents on live networks by isolating interactions within a controlled environment while grounding them in real-time content streams from the Bluesky ecosystem. The system features an asynchronous orc

Damien F. G. Minenna, Guillaume Dilasser, Robin Penavaire, Valerio Calvelli

Superconducting magnets for particle accelerators are particularly challenging to design because they involve a large number of coupled physical phenomena and the management of complex datasets. Artificial Intelligence (AI), including machine learning and advanced optimisation techniques, offers promising approaches to address these challenges and accelerate

Ang Li, Ling-Na Wu, Li You

Spin squeezing serves as both a fundamental witness of quantum entanglement and a critical resource for quantum-enhanced metrology. While generating substantial spin squeezing in finite-range interacting systems remains challenging, such capability is important for advancing quantum technologies. In this work, we develop an optimal control strategy for achie

Yi Hu, Leying Yi, Emily Davis, Finn Carter

Diffusion-based editing has rapidly evolved from curated inpainting tools into general-purpose editors spanning text-guided instruction following, mask-localized edits, drag-based geometric manipulation, exemplar transfer, and training-free composition systems. Despite strong empirical progress, the field lacks a unified treatment of core desiderata that gov

Thomas Tanay, Mohammed Brahimi, Michal Nazarczuk, Qingwen Zhang

Synthesizing novel views from monocular videos of dynamic scenes remains a challenging problem. Scene-specific methods that optimize 4D representations with explicit motion priors often break down in highly dynamic regions where multi-view information is hard to exploit. Diffusion-based approaches that integrate camera control into large pre-trained models c

Rosario Leonardi, Antonino Furnari, Francesco Ragusa, Giovanni Maria Farinella

In this work, we explore the role of synthetic data in improving the detection of Hand-Object Interactions from egocentric images. Through extensive experimentation and comparative analysis on VISOR, EgoHOS, and ENIGMA-51 datasets, our findings demonstrate the potential of synthetic data to significantly improve HOI detection, particularly when real labeled

Jijun Lu, Yifan Chen, Libang Chen, Yiqiang Zhou

Single-pixel imaging (SPI) is a promising imaging modality with distinctive advantages in strongly perturbed environments. Existing SPI methods lack physical sparsity constraints and overlook the integration of local and global features, leading to severe noise vulnerability, structural distortions and blurred details. To address these limitations, we propos

Akitoshi Hoshiya, Kouichi Taira

In this paper, we consider the dispersive estimates for Schr\"odinger operators with Coulomb-like decaying potentials, such as $V(x)=-c|x|^{-\mu}$ for $|x|\gg 1$ with $0<\mu<2$, in one dimension. As an application, we establish both the standard and orthonormal Strichartz estimates for this model. One of the difficulties here is that perturbation arguments,

Leonardo Medrano Sandonas, David Balcells, Anton Bochkarev, Jacqueline M. Cole

Artificial intelligence is transforming molecular and materials science, but its growing computational and data demands raise critical sustainability challenges. In this Perspective, we examine resource considerations across the AI-driven discovery pipeline--from quantum-mechanical (QM) data generation and model training to automated, self-driving research w

Ron M. Adin, Tomer Bauer

Carnevale, Schein and Voll proved self-reciprocity of the generalized Igusa functions, and Maglione and Voll did the same for the Hall-Littlewood-Schubert series. We introduce a simultaneous generalization and refinement of these two rational functions, and prove that it satisfies a self-reciprocity property. This answers a problem posed by Maglione and Voll

Latent Labs Team, Sebastian M. Schmon, Daniella Pretorius, Simon Mathis

Drug discovery relies on iterative expert workflows that are slow to parallelize and difficult to scale. Here we introduce Latent-Y, an AI agent that autonomously executes complete antibody design campaigns from text prompts, covering literature review, target analysis, epitope identification, candidate design, computational validation, and selection of lab-

Ethan Carlier, Nicolas Chamel

Over the past decade, gravitational-wave astronomy has opened a new window onto the extreme states of matter inside compact stars. At some point during the inspiral of a binary system, each star starts to experience adiabatic tides, characterized by tidal deformabilities. The dominant tidal deformability, first measured with the GW170817 event, has already c

Ren-Rui Liu, Jun Fan, Lei Shi, Zheng-Chu Guo

This paper focuses on the problem of unbounded density ratio estimation -- an understudied yet critical challenge in statistical learning -- and its application to covariate shift adaptation. Much of the existing literature assumes that the density ratio is either uniformly bounded or unbounded but known exactly. These conditions are often violated in practi

Valentin Breaz, Miguel Munoz Zuniga, Olivier Zahm, Richard Wilkinson

To reduce the cost of estimating the probability of a rare event involving a very large number of random parameters, we propose a new strategy for dimension reduction coupled with a surrogate model for the expensive part of the algorithm. To this end, we extend the Ordinary Kriging Active Subspace (OK-AS) method into a sequential version. Our approach consis

Chenyang Zuo, Siqi Fan, Yizhen Luo, Zaiqing Nie

Retrosynthetic planning is a fundamental task in organic chemistry, yet remains challenging due to its combinatorial complexity. To address this, conventional approaches typically rely on hybrid frameworks that combine single-step predictions with external search heuristics, inevitably fracturing the logical coherence between local molecular transformations

Mauro Marchese, Dario Tagliaferri, Henk Wymeersch, Musa Furkan Keskin

While Third Generation Partnership Project (3GPP) has confirmed orthogonal frequency division multiplexing (OFDM) as the baseline waveform for sixth-generation (6G), its performance is severely compromised in the high-mobility scenarios envisioned for 6G. Building upon the GEARBOX-PHY vision, we present gear-switching OFDM (GS-OFDM): a unified framework in w

Jérôme Carrand

Using only continuous partitions of unity, we provide equivalent definitions for the metric, topological and topological tail entropies and pressures of a continuous self-map of a compact set, as well as their conditional versions. A tail variational principle for these new definitions is proved. We extend Downarowicz's notions of candidates and entropy stru

Giada Forte, Enzo Orlandini, Davide Marenduzzo

Bacterial chromosome replication occurs in the absence of a canonical spindle apparatus; yet it reliably produces organised and segregated genomes. While both passive and active mechanisms have been investigated, DNA replication itself is a non-equilibrium process that continuously generates new genetic material and reorganizes the nucleoid. Here, we investi

Qigang Liang, Xuejun Xu, Qingquan Zhang

In this paper, we propose an adaptive multilevel preconditioned Helmholtz-Jacobi-Davidson (PHJD) method for the Maxwell eigenvalue problem with singularities. The key idea in this work is to employ the local multilevel method for preconditioning the Jacobi-Davidson correction equation. It is shown that our convergence factor is quasi-optimal, which means the

Hyeonho Noh, Jonggyu Jang

This paper proposes an $\alpha$-fair multistatic integrated sensing and communication (ISAC) framework for multi-user multi-input multi-output (MIMO)-orthogonal frequency division multiplexing (OFDM) systems, where communication users act as passive bistatic receivers to enable multistatic sensing. Unlike existing works that optimize aggregate sensing metric

Andreas Abel, Nils Anders Danielsson, Oskar Eriksson

We present a graded modal type theory, a dependent type theory with grades that can be used to enforce various properties of the code. The theory has $\Pi$-types, weak and strong $\Sigma$-types, natural numbers, an empty type, and a universe, and we also extend the theory with weak and strong unit types and graded $\Sigma$-types. The theory is parameterized