November 2025 arXiv papers — page 31

Umberto Michelucci, Francesca Venturini

Machine learning (ML) is increasingly adopted in scientific research, yet the quality and reliability of results often depend on how experiments are designed and documented. Poor baselines, inconsistent preprocessing, or insufficient validation can lead to misleading conclusions about model performance. This paper presents a practical and structured guide fo

V. V. Bavula

This paper is a finishing touch to the (over 200 years) {\em classical} `Galois Theory' of {\em arbitrary} finite field extensions, i.e. the goal of it is to describe intermediate subfields of an arbitrary finite field extension via {\em invariants} of `natural/obvious' objects that are associated with subfields via two Galois-type correspondences. The class

Arthur Forey, Javier Fresán, Emmanuel Kowalski, Yuval Wigderson

We construct explicit families of graphs whose eigenvalues are asymptotically distributed according to Wigner's semicircle law; in other words, that are spectrally indistinguishable from random graphs. However, in other respects they are strikingly dissimilar from random graphs; for example, they are $K_{2,3}$-free graphs with almost the maximum possible edg

Kazuki Nakajima, Yuya Sasaki, Takeaki Uno, Masaki Aida

Higher-order networks, naturally described as hypergraphs, are essential for modeling real-world systems involving interactions among three or more entities. Stochastic block models offer a principled framework for characterizing mesoscale organization, yet their extension to hypergraphs involves a trade-off between expressive power and computational complex

Dario Corona, Stefano Nardulli, Ramon Oliver-Bonafoux, Giandomenico Orlandi

We provide a multiplicity result for solutions of time-independent Gross-Pitaevskii equations on closed Riemannian manifolds. Such solutions arise as (possibly non-minimizing) critical points of the Ginzburg-Landau energy having prescribed momentum according to a given tangent velocity field. Lower bounds on the multiplicity of solutions are obtained in term

Qiao Wang

This paper reports the first gas sensor based on the plastic inorganic semiconductor GaPS4, pioneering the application of plastic inorganic semiconductors in the field of gas sensing. Unlike traditional rigid sensors, this device leverages the unique layered structure and ultra-wide bandgap of GaPS4 to achieve high sensitivity and selectivity in detecting NO

L. Fellay, M. -A. Dupret, P. A. Kołaczek-Szymański

In binary systems, studying tidal interactions is key to understanding the evolution of binary populations. The primary dissipation process occurring in stars with radiative envelopes is believed to be radiative damping of high-radial-order tidally excited oscillations, which is in agreement with observations of most binary systems. However, recent studies h

Fong Yew Leong, Jaeyoung Kwak, Zhengwei Ge, Chin Chun Ooi

The risk of indoor airborne transmission among co-located individuals is generally non-uniform, which remains a critical challenge for public health modelling. Thus, we present CompARE, an integrated risk assessment framework for indoor airborne disease transmission that reveals a striking bimodal distribution of infection risk driven by airflow dynamics and

Mohamed Shahawy, Julien de Castelnau, Paolo Ienne

Task-level parallelism (TLP) is a widely used approach in software where independent tasks are dynamically created and scheduled at runtime. Recent systems have explored architectural support for TLP on field-programmable gate arrays (FPGAs), often leveraging high-level synthesis (HLS) to create processing elements (PEs). In this paper, we present Bombyx, a

Chin-Hung Chen, Yan Wu, Wim van Houtum, Alex Alvarado

Digital Audio Broadcasting (DAB)-like systems employ differentially encoded (DE) phase-shift keying (PSK) for transmission. While turbo-DE-PSK receivers offer substantial performance gains through iterative decoding by making the DE-PSK an inner code, they rely on accurate channel estimation without pilots, which is a key challenge in DAB-like scenarios. Thi

Laura Boca de Giuli, Samuel Mallick, Alessio La Bella, Azita Dabiri

This paper presents a model predictive control (MPC) framework leveraging an ensemble of data-based models to optimally control complex systems under multiple operating conditions. A novel combination rule for ensemble models is proposed, based on the statistical Mahalanobis distance, enabling the ensemble weights to suitably vary across the prediction windo

Aleksey Turchanov

In contrast to the extensive literature on thin tunnel junctions and traditional SQUID geometries, there is almost no quantitative experimental data on magnetic control of the Josephson phase in thick SNS weak links. The standard view is that in such compact structures without macroscopic loops the local magnetic coupling to the phase is negligibly small, wh

Genís Plaja-Roglans, Yun-Ning Hung, Xavier Serra, Igor Pereira

Separating the individual elements in a musical mixture is an essential process for music analysis and practice. While this is generally addressed using neural networks optimized to mask or transform the time-frequency representation of a mixture to extract the target sources, the flexibility and generalization capabilities of generative diffusion models are

Xiao-Yu Yan, Wen-Hui Ai

A Borel probability measure \( \mu \) with compact support on \( \mathbb{R}^n \) is called spectral measure if there exists a discrete set \( \Lambda \subset \mathbb{R}^n \) such that \( E_\Lambda := \{e^{2\pi i \langle \lambda, x \rangle}: \lambda \in \Lambda\} \) forms an orthonormal basis of \( L^2(\mu) \). In this paper, we study the spectrality and non-

Chin-Hung Chen, Ivana Nikoloska, Wim van Houtum, Yan Wu

This paper presents a fully blind phase-aware expectation-maximization (EM) algorithm for OFDM systems with the phase-shift keying (PSK) modulation. We address the well-known local maximum problem of the EM algorithm for blind channel estimation. This is primarily caused by the unknown phase ambiguity in the channel estimates, which conventional blind EM est

Tae-Min Choi, Tae Kyeong Jeong, Garam Kim, Jaemin Lee

Recent advances in multimodal large language models (LLMs) have highlighted their potential for medical and surgical applications. However, existing surgical datasets predominantly adopt a Visual Question Answering (VQA) format with heterogeneous taxonomies and lack support for pixel-level segmentation, limiting consistent evaluation and applicability. We pr

Mehyar Mlaweh, Tristan Cazenave, Ines Alaya

The Ribonucleic Acid (RNA) inverse folding problem, designing nucleotide sequences that fold into specific tertiary structures, is a fundamental computational biology problem with important applications in synthetic biology and bioengineering. The design of complex three-dimensional RNA architectures remains computationally demanding and mostly unresolved, a

Munish Rathee, Boris Bačić, Maryam Doborjeh

This paper presents the SIFT-SNN framework, a low-latency neuromorphic signal-processing pipeline for real-time detection of structural anomalies in transport infrastructure. The proposed approach integrates Scale-Invariant Feature Transform (SIFT) for spatial feature encoding with a latency-driven spike conversion layer and a Leaky Integrate-and-Fire (LIF)

G. Angloher, M. R. Bharadwaj, A. Böhmer, M. Cababie

Thallium-doped sodium iodide (NaI(Tl)) scintillation detectors play an important role in the field of direct dark matter (DM) searches. The DAMA/LIBRA experiment stands out for its reported observation of an annually modulating DM-like signal, which is in direct contrast with other results. To accurately calibrate the energies of nuclear recoil signals with

Ghada Jaber, Mohamed Ali Zormati, Walid Cavelius, Louka Chapiro

The convergence of the Internet of Things (IoT) and 5G technologies is transforming modern communication systems by enabling massive connectivity, low latency, and high-speed data transmission. In this evolving landscape, Content-Centric Networking (CCN) is emerging as a promising alternative to traditional Internet Protocol (IP)-based architectures. CCN off

Haksoo Lim, Jaehoon Lee, Sewon Park, Minjung Kim

Score-based generative models (SGMs) have demonstrated unparalleled sampling quality and diversity in numerous fields, such as image generation, voice synthesis, and tabular data synthesis, etc. Inspired by those outstanding results, we apply SGMs to synthesize time-series by learning its conditional score function. To this end, we present a conditional scor

Kai Kugler

We present the first systematic investigation of Martin's Law - the empirical relationship between word frequency and polysemy - in text generated by neural language models during training. Using DBSCAN clustering of contextualized embeddings as an operationalization of word senses, we analyze four Pythia models (70M-1B parameters) across 30 training checkpo

Benjamin Hollering, Elia Mazzucchelli, Matteo Parisi, Bernd Sturmfels

We consider configurations of lines in 3-space with incidences prescribed by a graph. This defines a subvariety in a product of Grassmannians. Leveraging a connection with rigidity theory in the plane, for any graph, we determine the dimension of the incidence variety and characterize when it is irreducible or a complete intersection. We study its multidegre

Mohammad Pouranvari

We study relaxation spectra of a quadratic spinless--fermion helical chain with an Aubry--Andre--type quasiperiodic potential and a single N--th neighbor (helical) hopping. Dissipation and pumping are introduced via local linear Lindblad jump operators and treated exactly using the third--quantization / Majorana covariance formalism. Focusing on periodic bou

Stefanos Koutoupis, Michaela Areti Zervou, Konstantinos Kontras, Maarten De Vos

Learning joint representations across multiple modalities remains a central challenge in multimodal machine learning. Prevailing approaches predominantly operate in pairwise settings, aligning two modalities at a time. While some recent methods aim to capture higher-order interactions among multiple modalities, they often overlook or insufficiently preserve

Vladimir Osipov, Alexander Dulebo, David J. Webb

We show that with the use of a short wavelength (520nm) femtosecond laser source, Two-Photon Polymerization (2PP) fabrication of micro-membrane designs with channel sizes down to 1 {\mu}m is possible, using commercial photopolymers OrmoComp and FemtoBond. This approach, involving the flexible design and direct manufacturing of micro-filters and micro-membran

Chien-Hua Chen

In this paper, we develop a view of self-isogenous modular polynomials and the $\mathfrak{l}$-cyclic isogeny graph for CM Drinfeld modules of arbitrary rank $r$. On the computational side, we give an explicit procedure to construct the modular polynomial $\Phi_{J,\mathfrak{a}}(X,X)$ for Drinfeld modules of rank $r\geqslant 3$ with $\mathfrak{a}$ a prime idea

Zhuoya Cao, Ya-Ping Li, Douglas N. C. Lin, Shude Mao

Complex structures, including sharp edges, rings and gaps, have been commonly observed in protoplanetary disks with or without planetary candidates. Here we consider the possibility that they are the intrinsic consequences of angular momentum transfer mechanisms, and investigate how they may influence the dynamical evolution of embedded planets. With the aid

Tao Wang, Cyril Touzé, Haiqin Li, Qian Ding

This article is devoted to the application of the parametrisation method for invariant manifold with a complex normal form style (CNF), for the derivation of high-order approximations of underdamped nonlinear dispersion relationships for periodic structures, more specifically by considering the case of a locally resonant metamaterial chain incorporating damp

Mikhail Tsitsvero, Atsuyuki Nakao, Hisaki Ikebata

Experimental validation of chemical processes is slow and costly, limiting exploration in materials discovery. Machine learning can prioritize promising candidates, but existing data in patents and literature is heterogeneous and difficult to use. We introduce a universal directed-tree process-graph representation that unifies unstructured text, molecular st

Darryl Biggar, Mohammad Reza Hesamzadeh

Electric power systems are increasingly turning to energy storage systems to balance supply and demand. But how much storage is required? What is the optimal volume of storage in a power system and on what does it depend? In addition, what form of hedge contracts do storage facilities require? We answer these questions in the special case in which the uncert

Hrishabh Bharadwaj, Sukanta Dutta

We estimate the accuracy with which the coefficient of the lepton flavour-violating dimension-six operators can be measured at the proposed electron-proton collider. Cuts-based analysis is performed to compute the signal significance at the centre of mass energy of 1.3 TeV, with a total integrated luminosity upto $\sim$ab$^{-1}$. Using the optimal observable

JunPeng Pan, Ya-Ping Li, Yi-Xian Chen, Shigeru Ida

Migration typically occurs during the formation of planets and is closely linked to the planetary formation process. In classical theories of non-accreting planetary migration, both type I and type II migration typically result in inward migration, which is hard to align with the architecture of the planetary systems.In this work, we conduct systematic, high

Kirti Bhagat, Shaily Bhatt, Athul Velagapudi, Aditya Vashistha

Millions of users across the globe turn to AI chatbots for their creative needs, inviting widespread interest in understanding how they represent diverse cultures. However, evaluating cultural representations in open-ended tasks remains challenging and underexplored. In this work, we present TALES, an evaluation of cultural misrepresentations in LLM-generate

Mengni Li, You Li

This paper is devoted to a complete classification on the existence and nonexistence results of viscosity solutions to the general Dirichlet problem for a class of eigenvalue type equations. With the distance function included in the right-hand side, this type of equations can be degenerate and (or) singular near the boundary of uniformly convex domains. One

Heiko Oppel, Andreas Spilz, Michael Munz

Denoising Diffusion Probabilistic Models (DDPMs) can generate synthetic timeseries data to help improve the performance of a classifier, but their sampling process is computationally expensive. We address this by combining implicit diffusion models with a novel Sawtooth Sampler that accelerates the reverse process and can be applied to any pretrained diffusi

Alailton J. Alves Junior, Daniel Barbosa, Ricardo A. S. Fernandes, Denis V. Coury

The increasing integration of Inverter-Based Resources (IBRs) is reshaping fault current characteristics, presenting significant challenges to traditional protection and fault location methods. This paper addresses a key limitation in fault location within wind farm collector networks, i.e., one-terminal phasor-based methods become inaccurate when IBRs are e

Qian Li, Qianchuan Wang, Junji Jia

The scattering of charged massive scalar waves by Kerr-Newman black holes, with incidence along the equatorial plane, is investigated in this work. The differential scattering cross section is computed using the partial wave method, with the forward divergence handled via the series reduction technique. For the first time, we systematically examine the influ

Weitian Wang, Lukas Meiner, Rai Shubham, Cecilia De La Parra

The Visual Geometry Grounded Transformer (VGGT) marks a significant leap forward in 3D scene reconstruction, as it is the first model that directly infers all key 3D attributes (camera poses, depths, and dense geometry) jointly in one pass. However, this joint inference mechanism requires global attention layers that perform all-to-all attention computation

Vlatko Crnković

It is well known that the Minkowski dimension of spiral trajectories near a non-degenerate focus in analytic (smooth) systems is in one-to-one correspondence with the cyclicity of the focus in generic unfoldings. We give a complete fractal treatment, in terms of the Minkowski dimension and (non-)degeneracy, of spiral trajectories near pseudo foci of piecewis

Jia-Yi Lu, Yi-Qiang Lin, Kai-Xin Ye, Xin-Yu Zhao

Recent studies suggest that the tetragonal phase of the Ruddlesden-Popper (RP) bilayer nickelate, La$_3$Ni$_2$O$_7$ or La$_2$PrNi$_2$O$_7$, which is stabilized under high pressures, is responsible for high-temperature superconductivity (HTSC). In this context, realization of the tetragonal phase at ambient pressure could be a rational step to achieve the goa

Jewel Benny, Narahari N. Moudhgalya, Mujeev Khan, Hemant Kumar Meena

In this work, we introduce an innovative approach to estimate the vital signs of multiple human subjects simultaneously in a non-contact way using a Frequency Modulated Continuous Wave (FMCW) radar-based system. Traditional vital sign monitoring methods often face significant limitations, including subject discomfort with wearable devices, challenges in cali

Ivan Kalthoff, Marcel Rey, Raphael Wittkowski

Wave-guide-based physical systems provide a promising route toward energy-efficient analog computing beyond traditional electronics. Within this landscape, acoustic neural networks represent a promising approach for achieving low-power computation in environments where electronics are inefficient or limited, yet their systematic design has remained largely u

Martin Jacquet, Marvin Harms, Kostas Alexis

This paper introduces a neural Nonlinear Model Predictive Control (NMPC) framework for mapless, collision-free navigation in unknown environments with Aerial Robots, using onboard range sensing. We leverage deep neural networks to encode a single range image, capturing all the available information about the environment, into a Signed Distance Function (SDF)

Piotr Arcab, Mikolaj Rogalski, Karolina Niedziela, Anna Chwastowicz

Deep-ultraviolet (DUV) microscopy can provide label-free biochemical contrast by exploiting the intrinsic absorption of nucleic acids, proteins and lipids, offering chemically specific morphological information that complements structural optical thickness contrast from phase-sensitive imaging. However, existing DUV microscopes typically rely on specialized

Ivo Bueno, Ruikun Hou, Babette Bühler, Tim Fütterer

Observation of classroom interactions can provide concrete feedback to teachers, but current methods rely on manual annotation, which is resource-intensive and hard to scale. This work explores AI-driven analysis of classroom recordings, focusing on multimodal instructional activity and discourse recognition as a foundation for actionable feedback. Using a d

Chenyu Liu, Hongze Chen, Jingzhi Bao, Lingting Zhu

Despite major advances brought by diffusion-based models, current 3D texture generation systems remain hindered by cross-view inconsistency -- textures that appear convincing from one viewpoint often fail to align across others. We find that this issue arises from attention ambiguity, where unstructured full attention is applied indiscriminately across token

Xinyi Zhang, Liang Liang, Anastasia Ailamaki, Jianliang Xu

Indexes are critical for efficient data retrieval and updates in modern databases. Recent advances in machine learning have led to the development of learned indexes, which model the cumulative distribution function of data to predict search positions and accelerate query processing. While learned indexes substantially outperform traditional structures for p

Bingxue Tao

We provide a general sufficient condition for extendability of quasimorphisms on subgroups. This condition recovers the result of Hull--Osin on quasimorphisms on hyperbolically embedded subgroups, and the proof given in this paper is much simpler. We also obtain new results for quasimorphisms on normal subgroups. One result is that for a group $G$ and its no

Vicente P. Soloviev, Michal Krompiec

Portfolio optimization is a cornerstone of financial decision-making, traditionally relying on classical algorithms to balance risk and return. Recent advances in quantum computing offer a promising alternative, leveraging quantum algorithms to efficiently explore complex solution spaces and potentially outperform classical methods in high-dimensional settin

Luigi Catello, Italo Napolitano, Davide Salzano, Mario di Bernardo

We propose a Reinforcement Learning framework for sparse indirect control of large-scale multi-agent systems, where few controlled agents shape the collective behavior of many uncontrolled agents. The approach addresses this multi-scale challenge by coupling ODEs (modeling controlled agents) with a PDE (describing the uncontrolled population density), captur

Kai Sheng

In this paper, by adapting the perturbation method, we study normalized standing wave solutions for the following nonlinear Schr\"odinger-Bopp-Podolsky system: - Delta u + q(x) phi u = omega u + f(u) in Omega, - Delta phi + a^2 Delta^2 phi = q(x) u^2 in Omega, where Omega is a smooth bounded domain in R^3, a > 0, and omega is the Lagrange multiplier associat

Stefano Berrone, Lorenzo Neva, Moreno Pintore, Gioana Teora

In this paper, we present a new polygonal finite element method, called the Zipped Finite Element Method, for star-shaped polygons. The proposed approach constructs high-order shape functions as linear combinations of standard finite element basis functions defined on a local trivial sub-triangulation of each element. This refinement is used solely for the c

Leonardo Regano, Daniele Canavese, Cataldo Basile, Marco Torchiano

Evaluating the effectiveness of software protection is crucial for selecting the most effective methods to safeguard assets within software applications. Obfuscation involves techniques that deliberately modify software to make it more challenging to understand and reverse-engineer, while maintaining its original functionality. Although obfuscation is widely

Jules Decaestecker, Nicolas Vigne

Achieving both high accuracy and topological continuity in road segmentation from satellite imagery is a critical goal for applications ranging from urban planning to disaster response. State-of-the-art methods often rely on Vision Transformers, which excel at capturing global context, yet their quadratic complexity is a significant barrier to efficient depl

Amiya Chowdhury, Acacio Rincon Romero, Grazziela Figueredo, Tanvir Hussain

High-Entropy/multicomponent rare-earth oxides (HECs and MCCs) show promise as alternative materials for thermal barrier coatings (TBC) with the ability to tailor properties based on the combination of rare-earth elements present. By enabling the substitution of scarce or supply-risk rare-earths with more readily available alternatives while maintaining compa

Milan Rosko

We develop a proof-theoretic analysis of the Operational Standard of Matsas, Pleitez, Saa, Vanzella (2024) showing that admissible measurement in Minkowski Spacetime yields only finite observational sequences and thereby restricts the class of physically meaningful propositions to those admitting terminating extraction procedures or uniform stability conditi

Wenbo Fan, Weihua Gu

This paper introduces fly-by transit (FBT), a novel mobility system that employs modular mini-electric vehicles (mini-EVs) to provide door-to-door shared mobility with minimal stops. Unlike existing modular minibus concepts that rely on in-motion coupling and passenger transfers -- technologies unlikely to mature soon -- FBT lowers the technological barriers

Minling Li, Changzhen Sun, Chao Wang, Dongyi Wei

The transition mechanism from laminar flow to turbulent flow is a central problem in hydrodynamic stability theory. To shed light on this transition mechanism, Trefethen et al.({\it \small Science 1993}) proposed the transition threshold problem, aiming to quantify the magnitude of perturbations required to trigger instability and determine their scaling wit

Jon Merladet Urigüen, Ashot Minasyan

This paper focuses on studying properties of amalgamated free products $G=G_1*_{G_0} G_2$, where the amalgamated subgroup $G_0$ is virtually cyclic. First, we prove that if the factors $G_1$ and $G_2$ are finitely generated virtually abelian groups then $G$ can be mapped to another virtually abelian group so that this homomorphism is injective on each factor

Xubin Hu, Liming Ma

In this manuscript, we investigate the automorphism group of a maximal function field with the second largest possible genus over finite field of even characteristic, which is called the Abd\'on--Torres function field. As an application, we determine the automorphism groups of one-point algebraic geometry codes from such a maximal function field. It turns ou

Tien Dat Hoang

Backdoor attacks pose severe security threats to deep neural networks by embedding malicious triggers that force misclassification. While machine unlearning techniques can remove backdoor behaviors, current methods lack transparency and real-time interpretability. This paper introduces a novel framework that integrates Gradient-weighted Class Activation Mapp

Wanyun Chen, Haoqi Li, Chih-Ting Lu, Qiulei Wang

The Galactic Center GeV Excess (GCE) remains a compelling but enigmatic signal from the inner region of our galaxy. Muonphilic dark matter (DM), which couples exclusively to muons via a new mediator, provides a viable explanation for the GCE and relic density while naturally evading constraints from direct detection, collider searches and other multi-messeng

The HERA Collaboration, Zuhra Abdurashidova, Tyrone Adams, James E. Aguirre

We report the first upper limits on the power spectrum of 21-cm fluctuations during the Epoch of Reionization and Cosmic Dawn from Phase II of the Hydrogen Epoch of Reionization Array (HERA) experiment. HERA Phase II constitutes several significant improvements in the signal chain compared to Phase I, most notably resulting in expanded frequency bandwidth, f

Chunkai Xu

We establish a stronger Bogomolov--Gieseker type inequality for slope-semistable sheaves on the smooth quintic threefold. Our approach combines a refined restriction theorem for tilt-stable objects with explicit Clifford-type bounds for semistable bundles on plane quintic curves. As a consequence, we obtain an explicit piecewise linear inequality on the Cher

Ivan Guo, Severin Nilsson, Johannes Wiesel

We extend the Benamou-Brenier formula from classical optimal transport to weak optimal transport and show that the barycentric optimal transport problem studied by Gozlan and Juillet has a dynamic analogue. We also investigate a martingale relaxation of this problem, and relate it to the martingale Benamou-Brenier formula of Backhoff-Veraguas, Beiglb\"ock, H

Gebhard Martin, Giacomo Mezzedimi, Davide Cesare Veniani

Lehmer's number $\lambda_{10}$ is the smallest dynamical degree greater than $1$ that can occur for an automorphism of an algebraic surface. We show that $\lambda_{10}$ cannot be realized by automorphisms of Enriques surfaces in odd characteristic, extending a result of Oguiso over the complex numbers. In contrast, we prove that in characteristic $2$ there e

Yaoru Li, Heyu Si, Federico Landi, Pilar Oplustil Gallegos

Text-to-video (T2V) diffusion models have recently achieved impressive visual quality, yet most systems still generate silent clips and treat audio as a secondary concern. Existing audio-video generation pipelines typically decompose the task into cascaded stages, which accumulate errors across modalities and are trained under separate objectives. Recent joi

M. Muñoz Pérez

In this paper we will try to provide a solid form of intrinsic set theoretical optimism. In other words, we will try to vindicate G\"odel's views on phenomenology as a method for arriving at new axioms of ZFC in order to decide independent statements such as CH.

Xiaohang Yu, William Knottenbelt

We study the periodic behaviour of the dual logarithmic derivative operator $\mathcal{A}[f]=\mathrm{d}\ln f/\mathrm{d}\ln x$ in a complex analytic setting. We show that $\mathcal{A}$ admits genuinely nondegenerate period-$2$ orbits and identify a canonical explicit example. Motivated by this, we obtain a complete classification of all nondegenerate period-$2

Xinran Li

A/B testing plays a central role in data-driven product development, guiding launch decisions for new features and designs. However, treatment effect estimates are often noisy due to short horizons, early stopping, and slowly accumulating long-tail metrics, making early conclusions unreliable. A natural remedy is to pool information across related experiment

Boumediene Hamzi, Houman Owhadi

The recent proof of quasi-Gaussianity for the 2D stochastic Navier--Stokes (SNS) equations by Coe, Hairer, and Tolomeo establishes that the system's unique invariant measure is equivalent (mutually absolutely continuous) to the Gaussian measure of its corresponding linear Ornstein--Uhlenbeck (OU) process. While Gaussian process (GP) frameworks are increasing

Jamal Raiyn

This paper proposes a new strategy for collision avoidance system leveraging Time-to-Collision (TTC) metrics for handling cut-in scenarios, which are particularly challenging for autonomous vehicles (AVs). By integrating a deep learning with TTC calculations, the system predicts potential collisions and determines appropriate evasive actions compared to trad

Emanuele Di Bella, Willem A. de Graaf, Andrea Santi

In 1981 Antonyan classified the orbits of SL$(8,\mathbb{C})$ on $\bigwedge^4 \mathbb{C}^8$. This is an example of a $\theta$-group action as introduced and studied by Vinberg. The orbits of a $\theta$-group are divided into three classes: nilpotent, semisimple and mixed. We consider the action of SL$(8,\mathbb{R})$ on $\bigwedge^4 \mathbb{R}^8$ and classify

Huiyun Tang, Feifei Wang, Long Feng, Yang Li

Small and medium-sized enterprises (SMEs) play a crucial role in driving economic growth. Monitoring their financial performance and discovering relevant covariates are essential for risk assessment, business planning, and policy formulation. This paper focuses on predicting profits for SMEs. Two major challenges are faced in this study: 1) SMEs data are sto

Sutirtha Biswas, Kshitij Kumar Yadav

Accurate and efficient seismic response prediction is essential for the design of resilient structures. While the Finite Element Method (FEM) remains the standard for nonlinear seismic analysis, its high computational demands limit its scalability and real-time applicability. Recent developments in deep learning - particularly Convolutional Neural Networks (

Henrik Ueberschaer

Let $u$ be a solution of $\Delta u=Vu$ on $\mathbb{R}^d$, where $V$ be continuous, nonnegative and bounded. We prove that the condition $$\int_{r_j\leq|x|\leq r_j+1}|u(x)|^2dx\to 0,$$ along any sequence $(r_j)$, $r_j\nearrow+\infty$, implies $u\equiv 0$ on $\mathbb{R}^d$. In particular, this implies the Landis conjecture for solutions satisfying a sufficient

Ana Cordon-Avila, Mostafa Selim, Momen Abayazid

Respiratory motion limits the accuracy and precision of abdominal percutaneous procedures. In this paper, respiratory motion is compensated robotically using motion estimation models. Additionally, a teleoperated insertion is performed using proximity-based haptic feedback to guide physicians during insertion, enabling a radiation-free remote insertion for t

Qingyun Li, Shuran Ma, Junwei Luo, Yi Yu

With Transformers achieving outstanding performance on individual remote sensing (RS) tasks, we are now approaching the realization of a unified model that excels across multiple tasks through multi-task learning (MTL). Compared to single-task approaches, MTL methods offer improved generalization, enhanced scalability, and greater practical applicability. Re

Roman Rausch, David Jansen, Sukhbinder Singh, Román Orús

Large Language Models (LLMs) are very demanding in terms of their computational resources. Low-rank decompositions of LLM weights, e.g. via Singular Value Decomposition (SVD), is a promising approach for LLM compression, but presents several practical hurdles, e.g. selecting appropriate layer-wise ranks and getting rid of its parameter redundancy. In this wo

Yicheng Zhong, Peiji Yang, Zhisheng Wang

Recent advances in Large Language Models (LLMs) have transformed text-to-speech (TTS) synthesis, inspiring autoregressive frameworks that represent speech as sequences of discrete codec tokens. Among them, single-codebook TTS LLMs have emerged as compact and streamable architectures that jointly model semantic and acoustic integration. However, despite their

Jinhui Chen, Huadong Sun, Ping Wu, Baocai Wang

In high-renewable power systems, active-power disturbances are becoming larger and exhibit increasingly diverse time scales, which complicates frequency stability assessment under unanticipated events. This paper presents a response-based frequency stability assessment method that uses disturbance power, inferred from generator electrical responses, to provi

Pasqua D'Ambra, Fabio Durastante, Salvatore Filippone

Isogeometric analysis (IgA) offers enhanced approximation capabilities for the discretization of elliptic boundary-value problems, yet it results in large, sparse, and increasingly ill-conditioned linear systems due to higher interconnectivity among degrees of freedom. In particular, the discretization with tensor-product B-splines or NURBS of degree $p$ on

Mohammed Alruqimi, Luca Di Persio

This paper investigates an optimal integration of deep learning with financial models for robust asset price forecasting. Specifically, we developed a hybrid framework combining a Long Short-Term Memory (LSTM) network with the Merton-L\'evy jump-diffusion model. To optimise this framework, we employed the Grey Wolf Optimizer (GWO) for the LSTM hyperparameter

Juncheng Chen, Chao Xu, Yanjun Cao

Learning-based image matching critically depends on large-scale, diverse, and geometrically accurate training data. 3D Gaussian Splatting (3DGS) enables photorealistic novel-view synthesis and thus is attractive for data generation. However, its geometric inaccuracies and biased depth rendering currently prevent robust correspondence labeling. To address thi

Iryna Hurova, Alinjar Dan, Karl Kruusamäe, Arun Kumar Singh

In recent years, dual-arm manipulation has become an area of strong interest in robotics, with end-to-end learning emerging as the predominant strategy for solving bimanual tasks. A critical limitation of such learning-based approaches, however, is their difficulty in generalizing to novel scenarios, especially within cluttered environments. This paper prese

Didier Galmiche, Timo Lang, Daniel Méry, David Pym

We introduce Bifurcation Logic, BL, which combines a basic classical modality with separating conjunction * together with its naturally associated multiplicative implication, that is defined using the modal ordering. Specifically, a formula A*B is true at a world w if and only if each of A,B holds at worlds that are each above w, on separate branches of the

Caspar Oesterheld, Vincent Conitzer

We consider a setting in which a principal gets to choose which game from some given set is played by a group of agents. The principal would like to choose a game that favors one of the players, the social preferences of the players, or the principal's own preferences. Unfortunately, given the potential multiplicity of equilibria, it is conceptually unclear

Sena Bozdag, Olivier Roy

We revisit a recent puzzle about common knowledge, the ``sailboat" case (Lederman, 2018), and argue that Lewisian common knowledge allows us to reconcile the pre-theoretical intuition that certain facts are ``public" in such situations, while these facts cannot be common knowledge in the classical, iterative sense. The crux of the argument is to understand L

Joseph Y. Halpern, Rafael Pass

Perhaps the most prominent current definition of (actual) causality is due to Halpern and Pearl. It is defined using causal models (also known as structural equations models). We abstract the definition, extracting its key features, so that it can be applied to any other model where counterfactuals are defined. By abstracting the definition, we gain a number

Susanna Terron

We extend Jones' construction to obtain a surjective map from the Brown-Thompson group $F_3$ to the set of pointed links up to pointed isotopy. We then introduce an operation on $F_3$, and use it to define a new monoid $(F_3, \diamond)$, called the central monoid. Using the extended version of Jones' construction, we obtain a surjective monoid homomorphism f

María García Díaz, Adam Brandenburger, Giannicola Scarpa

We formulate and prove an Agreement Theorem for quantum mechanics (QM), describing when two agents, represented by separate laboratories, can or cannot maintain differing probability estimates of a shared quantum property of interest. Building on the classical framework (Aumann, 1976), we define the modality of ``common certainty" through a hierarchy of cert

Sullivan Hué, Sébastien Laurent, Ulrich Aiounou, Emmanuel Flachaire

Post-Double-Lasso is becoming the most popular method for estimating linear regression models with many covariates when the purpose is to obtain an accurate estimate of a parameter of interest, such as an average treatment effect. However, this method can suffer from substantial omitted variable bias in finite sample. We propose a new method called Post-Doub

Jewel Benny, Pranjal Mahajan, Srayan Sankar Chatterjee, Mohd Wajid

Recent developments in mmWave radar technologies have enabled the truly non-contact heart-rate (HR) and breath-rate (BR) measurement approaches, which provides a great ease in patient monitoring. Additionally, these technologies also provide opportunities to simultaneously detect HR and BR of multiple patients, which has become increasingly important for eff

Mridhula Venkatanarayanan, Vladislav Slama, Madhubanti Mukherjee, Andrea Vezzosi

Alpha-Formamidinium-tin-iodide (alpha-FASnI3) is a leading candidate for lead-free photovoltaic applications, adopting a nearly cubic structure at room temperature, but its stability remains limited by oxidation-driven degradation. Reliable first-principles modelling of the photovoltaic alpha-phase is further complicated by inconsistent structural models and

Akihiro Mizutani, Shun Kawakami, Go Kato

The decoy-state Bennett-Brassard 1984 (BB84) quantum key distribution (QKD) protocol is widely regarded as the de facto standard for practical implementations. On the receiver side, passive basis choice is attractive because it significantly reduces the need for random number generators and eliminates the need for optical modulators. Despite these advantages

Gerd Steinebach

Two new Rosenbrock methods for solving index-1 differential algebraic equations are presented. Rodas6P is a sixth-order method based on the same design principles as the Rodas3P, Rodas4P, and Rodas5P methods. Tsit5DA is based on an explicit solution approach for the differential equations and a linear-implicit approach for the algebraic equations. Such a fou

Shuhan Xia, Peipei Li, Xuannan Liu, Dongsen Zhang

The threat of Audio-Video (AV) forgery is rapidly evolving beyond human-centric deepfakes to include more diverse manipulations across complex natural scenes. However, existing benchmarks are still confined to DeepFake-based forgeries and single-granularity annotations, thus failing to capture the diversity and complexity of real-world forgery scenarios. To

Quentin Gabot, Teck-Yian Lim, Jérémy Fix, Joana Frontera-Pons

Convolutional neural networks have shown remarkable performance in recent years on various computer vision problems. However, the traditional convolutional neural network architecture lacks a critical property: shift equivariance and invariance, broken by downsampling and upsampling operations. Although data augmentation techniques can help the model learn t

Lin Xu, Li Pei, Jianshuai Wang, Zhouyi Hu

Beam alignment enables efficient, stable transmission and control of optical energy and information, which critically depend on precise monitoring and regulation of the three-dimensional (3D) relative positioning between fibers. This study introduces an approach to achieve more accurate 3D measurement of the spatial displacement between two optical fibers in