December 2025 arXiv papers — page 96

Chenyu Zhao, Yingxue Xu, Fengtao Zhou, Yihui Wang

Current multimodal survival prediction methods typically rely on pathology images (WSIs) and genomic data, both of which are high-dimensional and redundant, making it difficult to extract discriminative features from them and align different modalities. Moreover, using a simple survival follow-up label is insufficient to supervise such a complex task. To add

L. Prisinzano, M. Montalto, G. Piotto, P. M. Marrese

The ESA's PLAnetary Transits and Oscillations of Stars (PLATO) mission aims to detect planets orbiting around dwarfs and subgiant stars with spectral type F5 or later, including M-dwarfs. The PLATO Input Catalogue (PIC) contains all targets available for observation by the nominal science. The latest version, PIC2.1.0.1, focuses on the Southern PLATO field,

Eva dos Santos

Extensive air showers produced by the interaction of ultra-high-energy cosmic rays ($E > 10^{18}$ eV) in the Earth's atmosphere provide a challenging yet unique channel to probe hadronic interactions at the 100 TeV center-of-mass energy scale. Over more than 20 years of operation, the Pierre Auger Observatory has delivered invaluable insights into the modeli

Luca Benatti, Luciano Mari, Marco Rigoli, Alberto G. Setti

We show existence and optimal growth estimate for the weak inverse mean curvature flow issuing from a point, on manifolds with certain curvature and isoperimetric conditions. These theorems imply analogous ones for the flow issuing from relatively compact sets. Some of the results are obtained by proving new decay estimates for the Green kernel of the $p$-La

Henrik Schumacher, Jannik Rönsch, Thorsten Hohage, Max Wardetzky

We employ the so-called tangent-point energy as Tikhonov regularizer for ill-conditioned inverse scattering problems in 3D. The tangent-point energy is a self-avoiding functional on the space of embedded surfaces that also penalizes surface roughness. Moreover, it features nice compactness and continuity properties. These allow us to show the well-posedness

Marc Kegel, Paula Truöl

In this short note, we prove that every closed, oriented, connected 3-manifold arises as Dehn surgery along a braid positive link.

Soham Sau, Michal Sedlák

In adaptive quantum circuits classical results of mid-circuit measurements determine the upcoming gates. This allows POVMs, quantum channels or more generally quantum instruments to be implemented sequentially, so that fewer qubits need to be used at each of the $N$ measurement steps. In this paper, we mathematically describe these problems via adaptive sequ

Muhammad Waseem

The photonic Spin Hall Effect (SHE) causes a polarization-dependent transverse shift of light at an interface. There is a significant research interest in controlling and enhancing the photonic SHE. In this paper, we theoretically investigate the microwave field control of the photonic SHE in a closed-loop $\Lambda$-type atomic system. We demonstrate that bo

Adarsha Shrestha, Basanta Pokharel, Binit Shrestha, Smriti Adhikari

Nepali, a low-resource language spoken by over 32 million people, continues to face challenges in natural language processing (NLP) due to its complex grammar, agglutinative morphology, and limited availability of high-quality corpora. Most efforts to date have centered on basic encoder architectures; they remain insufficient for Nepali-specific text generat

David A. Henriquez Bernal, Peter Nejjar

We study the asymmetric simple exclusion process (ASEP) on a segment $\{1,\ldots,b_N\}$ and are interested in its total variation distance to equilibrium when started from an initial configuration $\xi^{N}$. We provide a general result which gives the cutoff window and profile whenever a KPZ-type limit theorem is available for an extension of $\xi^{N}$ to $\

Jakub Szefer

This work presents the first thorough exploration of how reset operations in cloud-based quantum computers could be exploited to run quantum circuits for free. This forms a new type of attack on the economics of cloud-based quantum computers. All major quantum computing companies today offer access to their hardware through some type of cloud-based service.

Eva-Maria Hekkelman, Teun D. H. van Nuland, Jesse Reimann

We derive power counting formulas for ribbon graph amplitudes that were recently independently discovered in two contexts, namely as a generalization of the Kontsevich model, and as corresponding to a matrix model approach to the spectral action. The Feynman rules are based on divided difference functions of eigenvalues of an abstract Dirac operator. We obta

Menno Demmenie, Stefan Kooij, Daniel Bonn

We demonstrate a novel approach to three-dimensional (3D) printing of freeform ice structures by exploiting evaporative cooling. A micrometer-sized water jet is used to 3D print inside a vacuum chamber. The reduced ambient pressure leads to rapid evaporation of the extruded water, extracting latent heat, and quickly cooling the water well below 0 {\deg}C. On

Javier Chagoya, A. D. López-Hernández, M. Sabido

In this work, we revisit abelian S-duality in the context of higher gauge theory. By using a specific crossed module a set of transformations arise, which are known as the "thin" and "fat" transformations. The "fat" transformations are the ones introduced by hand to construct the S-dual theory. By utilizing crossed modules and higher gauge theory, we arrive

Stefan Kuyumdzhiev, Radostin Cholakov

Serving many task-specialized LLM variants is often limited by the large size of fine-tuned checkpoints and the resulting cold-start latency. Since fine-tuned weights differ from their base model by relatively small structured residuals, a natural approach is to represent them as compressed deltas. We propose a simple 1-bit delta scheme that stores only the

P. A. S. Guillen, J. F. Jesus, R. Valentim

In this work, we have \textbf{analysed} two kinematic parametrizations for $\Lambda(t)$CDM models, namely, the linear expansions $\Lambda(z)=\Lambda_0+\Lambda_1z$ and $Q(z)=Q_0+Q_1z$, where $Q$ is the interaction term. In the case of the $Q(z)$ parametrization, we have also tested the particular case of a constant interaction term, $Q(z)=Q_0$. In order to co

Philip Korman, Dieter S. Schmidt

We study analytical and computational aspects for Dirichlet problem on the unit ball $B$: $|x|<1$ in $R^n$, modeled on the equation \[ \Delta u +\lambda \left(u^p+u^q \right)=0, \;\; \mbox{in $B$}, \;\; u=0 \s \mbox{on $\partial B$}, \] with a positive parameter $\lambda$, and $1<p<\frac{n+2}{n-2}<q$, where $\frac{n+2}{n-2}$ is the critical power. It turns o

Ekaterina Artemova, Laurie Burchell, Daryna Dementieva, Shu Okabe

This tutorial (https://tum-nlp.github.io/low-resource-tutorial) is designed for NLP practitioners, researchers, and developers working with multilingual and low-resource languages who seek to create more equitable and socially impactful language technologies. Participants will walk away with a practical toolkit for building end-to-end NLP pipelines for under

Johannes Schmitt

For the pure $\psi$-class intersection numbers $D(\textbf{e})=\langle \tau_{e_1} \cdots \tau_{e_n} \rangle_g$ on the moduli space $\overline{\mathcal{M}}_{g,n}$ of stable curves, we determine for which choices of $\textbf{e}=(e_1, \ldots, e_n)$ the value of $D(\textbf{e})$ becomes extremal. The intersection number is minimal for powers of a single $\psi$-cla

Mitsuki Watanabe, Sosuke Amano, Kiyoharu Aizawa, Yoko Yamakata

Food image classification models are crucial for dietary management applications because they reduce the burden of manual meal logging. However, most publicly available datasets for training such models rely on web-crawled images, which often differ from users' real-world meal photos. In this work, we present FoodLogAthl-218, a food image dataset constructed

Arindam Banerjee, Tai Huy Ha, Vivek Bhabani Lama

We introduce and study the defect function associated to a pair of filtrations of ideals, which generalizes the symbolic defect of ideals. Under the assumption that the Rees algebra of one filtration is Noetherian and that a natural graded module measuring the interaction between the filtrations is finitely generated over it, we show that the corresponding d

L. Xia, P. J. M. van der Slot, M. Timmerkamp, C. Fallnich

We show that using strongly coupled dual-core waveguides for supercontinuum generation shifts the wavelength of the high-frequency dispersive waves towards shorter wavelengths, as compared to generation in a single-core waveguide having the same core dimensions. In a demonstration experiment, we launch ultrashort infrared pump pulses at 1-$\mu$m wavelength (

Shaun O'Donnell, Corlyn Regier, Sharad Mahatara, H. Cein Mandujano

The crystal and magnetic structures of the nitride antiperovskite Mn$_3$GeN reveals ferrimagnetic order stemming from a distorted kagome-derived lattice of the Mn atoms. Polycrystalline Mn$_3$GeN was synthesized via a solid-state reaction and characterized using neutron powder diffraction, DC magnetometry, and first-principles calculations. Rietveld refineme

Sven Hirsch, Yiyue Zhang

We characterize spin initial data sets that saturate the BPS bound in the asymptotically AdS setting. This includes both gravitational waves and rotating black holes in higher dimensions, and we establish a sharp dimension threshold in each case. A key ingredient in our argument is a theorem providing a general criterion for when an imaginary Killing spinor

Giacomo Ceccherini Silberstein, Daniela Tonon

The aim of this article is twofold. First, we develop a unified framework for viscosity solutions to both first-order Hamilton-Jacobi equations and semilinear Hamilton-Jacobi equations driven by the idiosyncratic operator, defined on the Wasserstein Space. Second, we establish a vanishing-viscosity limit-extending beyond the classical control-theoretic setti

Timothy L. H. Wee, Cheng Mao

To understand how hidden information can be extracted from statistical networks, planted models in random graphs have been the focus of intensive study in recent years. In this work, we consider the detection of a planted matching, i.e., an independent edge set, hidden in an Erd\H{o}s-R\'enyi random graph, which is formulated as a hypothesis testing problem.

Reza Hamzehofi

The $W$ subspace is defined as the subspace of the $n$-qubit Hilbert space spanned by states containing at most one excitation. In this work, a separability criterion is established for pure and mixed states supported in this subspace: if all reduced two-qubit subsystems are separable, then the entire $n$-qubit state is necessarily separable. This result mot

Fabian Haak, Philipp Schaer

Linguistic bias in online news and social media is widespread but difficult to measure. Yet, its identification and quantification remain difficult due to subjectivity, context dependence, and the scarcity of high-quality gold-label datasets. We aim to reduce annotation effort by leveraging pairwise comparison for bias annotation. To overcome the costliness

Arthur Capozzi

Political advertising on social media has fundamentally reshaped democratic deliberation, playing a central role in electoral campaigns and propaganda. However, its systemic impact remains largely theoretical or unexplored, raising critical concerns about institutional fairness and algorithmic transparency. This paper provides the first data-driven analysis

Tejaswani Dash, Gautam Datla, Anudeep Vurity, Tazeem Ahmad

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, underscoring the need for reliable and efficient predictive tools that support early intervention. Traditional diagnostic approaches rely on handcrafted features and clinician expertise, while machine learning methods improve reproducibility but often struggle to generalize across

Tejaswani Dash, Dinesh Karri, Anudeep Vurity, Gautam Datla

This paper introduces PolyPersona, a generative framework for synthesizing persona-conditioned survey responses across multiple domains. The framework instruction-tunes compact chat models using parameter-efficient LoRA adapters with 4-bit quantization under a resource-adaptive training setup. A dialogue-based data pipeline explicitly preserves persona cues,

Xiantao Zhang

Visually rich documents (VRDs) challenge retrieval-augmented generation (RAG) with layout-dependent semantics, brittle OCR, and evidence spread across complex figures and structured tables. This survey examines how Multimodal Large Language Models (MLLMs) are being used to make VRD retrieval practical for RAG. We organize the literature into three roles: Mod

Xianwei Cao, Dou Quan, Shuang Wang, Ning Huyan

Image retrieval-based cross-view geo-localization (IRCVGL) aims to match images captured from significantly different viewpoints, such as satellite and street-level images. Existing methods predominantly rely on learning robust global representations or implicit feature alignment, which often fail to model explicit spatial correspondences crucial for accurat

Emmanuel C. Chukwu, Rianne M. Schouten, Monique Tabak, Mykola Pechenizkiy

Counterfactual explanations are increasingly proposed as interpretable mechanisms to achieve algorithmic recourse. However, current counterfactual techniques for time series classification are predominantly designed with static data assumptions and focus on generating minimal input perturbations to flip model predictions. This paper argues that such approach

Mikhail M. Glazov, Atac Imamoglu

It is generally argued that Mermin-Wagner theorem excludes the possibility of long-range order in two dimensional bosonic systems at non-zero temperatures. In contrast, we show here that generic bilayer semiconductors could demonstrate true Bose-Einstein condensation of interlayer excitons. We show that the key requirements include (i) reduction of the inter

Quan Yuan, Xiaochen Li, Linkang Du, Min Chen

Causal inference plays a crucial role in scientific research across multiple disciplines. Estimating causal effects, particularly the average treatment effect (ATE), from observational data has garnered significant attention. However, computing the ATE from real-world observational data poses substantial privacy risks to users. Differential privacy, which of

Sneha Sree C., Dattesh Shanbhag, Sudhanya Chatterjee

Medical image registration is critical for aligning anatomical structures across imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound. Among existing techniques, non-rigid registration (NRR) is particularly challenging due to the need to capture complex anatomical deformations caused by physiological processes

Matthew Antrobus

We give a necessary and sufficient criterion for the solvability of $\operatorname{HH}^1(kP)$ as a Lie algebra, where $P$ is a $p$-group with $p$ odd, in terms of a directed graph constructed from the group $P$. This gives non-trivial results on the structure of such Lie algebras.

Nguyen Tien Dong, Minh-Anh Nguyen, Thanh Dat Hoang, Nguyen Tuan Ngoc

The rapid advancement of large language models (LLMs) has enabled new possibilities for applying artificial intelligence within the legal domain. Nonetheless, the complexity, hierarchical organization, and frequent revisions of Vietnamese legislation pose considerable challenges for evaluating how well these models interpret and utilize legal knowledge. To a

Rositsa Miteva, Pencho Markishki, Werner Pötzi, Momchil Dechev

The report presents a new initiative for the development of a network of ground-based stations for solar observations in the optical range. Three separate locations in Bulgaria have installed instrumentation for solar dedicated observations. The currently used telescopes (type, mounting, guiding systems) and designated filters (white-light, H-alpha) are desc

Yuichiro Nakano, Keisuke Fujii

We study the fair sampling properties of hybrid quantum-classical Markov chain Monte Carlo (MCMC) algorithms for combinatorial optimization problems with degenerate ground states. While quantum optimization heuristics such as quantum annealing and the quantum approximate optimization algorithm (QAOA) are known to induce biased sampling, hybrid quantum-classi

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

A search for long-lived heavy neutral leptons produced in B-meson decays and decaying to a $ \mu^\pm \pi^\mp$ final state is performed with data collected by the LHCb experiment in proton-proton collisions at a centre-of-mass energy of 13 TeV, corresponding to an integrated luminosity of $5\,\mathrm{fb}^{-1}$. The results are interpreted in both lepton-numbe

Zhiwen Yang, Jiaju Zhang, Yang Yi, Jian Liang

Medical image restoration (MedIR) aims to recover high-quality medical images from their low-quality counterparts. Recent advancements in MedIR have focused on All-in-One models capable of simultaneously addressing multiple different MedIR tasks. However, due to significant differences in both modality and degradation types, using a shared model for these di

David Samuel, Lucas Georges Gabriel Charpentier

This paper combines autoregressive and masked-diffusion training objectives without any architectural modifications, resulting in flexible language models that outperform single-objective models. Autoregressive modeling has been a popular approach, partly because of its training efficiency; however, that comes at the cost of sensitivity to overfitting. On th

Andrew C. Burgess, Lórien MacEnulty, Ethan D'Arcy, David Gavin

Streamlined prediction of the electronic properties of photoactive materials warrants a Density Functional Theory (DFT) based approach that (i) yields reliable bandgaps, (ii) is free of empirically tuned parameters, and (iii) exhibits low computational overhead. Here we show that for Cu2ZnSnS4 and Cu2ZnGeS4 kesterite photovoltaic materials, all three of thes

Bettina Eick, Patali Komma, Subhrajyoti Saha

The Lazard correspondence induces a close relation between the $p$-groups of maximal class and a certain type of Lie ring constructed from $p$-adic number fields. Our aim here is to investigate such Lie rings. In particular, we show that they are always finite. It then follows that they are nilpotent of small class. These results close an important gap in (E

Maggie Barker, Daniel Guo, Justin Palmeri, and Ridge Shepherd

In the paper, a program strength model was proposed to evaluate the performance of countries across different Olympic events. The model assessed how strong a country's program was in each event and also factored in the influence of past Olympic performances. The final medal counts from the Paris 2024 Olympic Games were used to validate the model and to deter

Samaya Nissanke, Nikhil Sarin, Chris Copperwheat, Sarah Antier

Multi-messenger astronomy will be transformed in the 2040s by an unprecedented volume of detections from next-generation gravitational wave, high-energy, and ultra-high energy neutrino, cosmic ray, and time domain observatories. This white paper, prepared for the European Southern Observatory (ESO) Expanding Horizons call, outlines the key science questions

C. Pan, J. L. An, P. Ring, X. H. Wu

Possible deformed neutron halos in silicon isotopes are investigated from both structure and reaction perspectives using the deformed relativistic Hartree-Bogoliubov theory in continuum (DRHBc) combined with the Glauber model. The experimental neutron separation energies of silicon isotopes are well reproduced by the DRHBc theory. Multiple halo criteria are

Zhongyi Zhang, Jun Zhan, Congcong Le, Hoi Chun Po

The recent discovery of high-temperature superconductivity in both bulk and thin-film bilayer nickelates has garnered significant attention. In this study, inspired by recent STM experiments on thin films, we investigate the quasiparticle interference (QPI) characteristics of bilayer nickelates in both normal and superconducting states to identify their Ferm

Changchun Feng, Laifa Tao, Lin Chen

Quantum state tomography (QST) faces exponential measurement requirements and noise sensitivity in multi-qubit systems, bottlenecking practical quantum technologies. We present a physics-informed neural network (PINN) framework integrating quantum mechanical constraints via adaptive weighting, a residual-and-attention-enhanced architecture, and differentiabl

Yifang Xu, Benxiang Zhai, Yunzhuo Sun, Ming Li

Recent advancements in diffusion-based technologies have made significant strides, particularly in identity-preserved portrait generation (IPG). However, when using multiple reference images from the same ID, existing methods typically produce lower-fidelity portraits and struggle to customize face attributes precisely. To address these issues, this paper pr

Subrata Das, Archisman Ghosh, Swaroop Ghosh

Cloud quantum platforms give users access to many backends with different qubit technologies, coupling layouts, and noise levels. The execution of a circuit, however, depends on internal allocation and routing policies that are not observable to the user. A provider may redirect jobs to more error-prone regions to conserve resources, balance load or for othe

Andreas Lolos, Theofilos Christodoulou, Aris L. Moustakas, Stergios Christodoulidis

In computational pathology, weak supervision has become the standard for deep learning due to the gigapixel scale of WSIs and the scarcity of pixel-level annotations, with Multiple Instance Learning (MIL) established as the principal framework for slide-level model training. In this paper, we introduce a novel setting for MIL methods, inspired by proceedings

Riku Ikarashi, Yuji Koike, Shinsuke Yoshida

We investigate the consistency between the collinear twist-3 factorization and the transverse-momentum-dependent (TMD) factorization for the transversely polarized hyperon production in semi-inclusive deep inelastic scattering, $ep\to e\Lambda^\uparrow X$. In particular, we focus on the contributions from the twist-3 fragmentation functions (FFs) and the TMD

Miriam Gutiérrez-Fernández, Karen López-Linares, Carlos Fambuena-Santos, María S. Guillem

Atrial fibrillation (AF) is the most prevalent sustained cardiac arrhythmia, and its clinical assessment requires accurate characterization of atrial electrical activity. Noninvasive electrocardiographic imaging (ECGI) combined with deep learning (DL) approaches for estimating intracardiac electrograms (EGMs) from body surface potentials (BSPMs) has shown pr

Yiheng Huang, Junhong Chen, Anqi Ning, Zhanhong Liang

Self-supervised monocular depth estimation has achieved notable success under daytime conditions. However, its performance deteriorates markedly at night due to low visibility and varying illumination, e.g., insufficient light causes textureless areas, and moving objects bring blurry regions. To this end, we propose a self-supervised framework named DASP tha

Thomas O. de Jong, Mircea Lazar, Siep Weiland, Florian Dörfler

This paper studies regularized data-enabled predictive control (DeePC) within a nonlinear framework and its relationship to subspace predictive control (SPC). The $\Pi$-regularization is extended to general basis functions and it is shown that, under suitable conditions, the resulting basis functions DeePC formulation constitutes a relaxation of basis functi

Xavier Tolsa

In this paper we explore the connection between quantitative rectifiability of measures and the $L^2$ boundedness of the codimension one Riesz transform. Among other things, we prove the following. Let $\mu$ be a Radon measure in $\mathbb R^{n+1}$ with growth of degree $n$ such that the $n$-dimensional Riesz transform $R_\mu$ is bounded in $L^2(\mu)$, and le

Luis Celso Chan Palomo, Scott A. Taylor

A nontrivial $\theta$-curve in $S^3$ is Brunnian if each of its cycles is the unknot. We show that if the exterior of a Brunnian $\theta$-curve is atoroidal, then it does not contain an essential annulus. Previously, Ozawa-Tsutsumi showed that there is no essential disc. Consequently, by Thurston's work, the exterior of an atoroidal Brunnian $\theta$-curve i

Marco Laudato

A data-driven surrogate framework to accelerate particle-resolved modelling of quasi-dilute suspensions of rigid, non-spherical particles in Stokes flow is introduced. A regularized-Stokeslet boundary element method (BEM) is implemented to compute hydrodynamic responses in canonical linear flows, focusing on the particle stresslet and angular velocity for sp

Ying Nie, Kai Han, Hongguang Li, Hang Zhou

The rapid scaling of Large Language Models (LLMs) has achieved remarkable performance, but it also leads to prohibitive memory costs. Existing parameter-efficient approaches such as pruning and quantization mainly compress pretrained models without enhancing architectural capacity, thereby hitting the representational ceiling of the base model. In this work,

Luka Milićević

Let $G_1, \dots, G_k$ be finite-dimensional vector spaces over a prime field $\mathbb{F}_p$. Let $V$ be a variety inside $G_1 \times \cdots \times G_k$ defined by a multilinear map. We show that if $|V| \geq c |G_1| \cdots |G_k|$, then $V$ contains a subvariety defined by at most $K(\log_{p} c^{-1} + 1)$ multilinear forms, where $K$ depends on $k$ only. This

Edward Gheorghita, Sebastian Wald, Andrea Pupić, Onur Hosten

Continuously operating atom-light interfaces represent a key prerequisite for steady-state quantum sensors and efficient quantum processors. Here, we demonstrate continuous accumulation of sub-Doppler-cooled atoms in a shallow intracavity dipole trap, realizing this regime. The key ingredient is a light-shift manipulation that creates spatially varying cooli

Shreyas Subramanian, Bala Krishnamoorthy, Pranav Murthy

Despite significant advances in optimizers for training, most research works use common scheduler choices like Cosine or exponential decay. In this paper, we study \emph{GreedyLR}, a novel scheduler that adaptively adjusts the learning rate during training based on the current loss. To validate the effectiveness of our proposed scheduler, we conduct experime

Riccardo Oliva, Farahdiba Zarin, Alice Zampolini Faustini, Armine Vardazaryan

Advanced Ovarian Cancer (AOC) is often diagnosed at an advanced stage with peritoneal carcinosis (PC). Fagotti score (FS) assessment at diagnostic laparoscopy (DL) guides treatment planning by estimating surgical resectability, but its subjective and operator-dependent nature limits reproducibility and widespread use. Videos of patients undergoing DL with co

Debendra Meher, Nikhil V. S. Avula, Sundaram Balasubramanian

An atomistic structural model for melt-quenched B$_2$O$_3$ glass has eluded the simulation community so far. The difficulty lies in the abundance of the six-membered boroxol rings - an intermediate-range order motif suggested through Raman and NMR spectroscopy - which is challenging to obtain in atomistic molecular dynamics simulations. Here, we report the d

José Paz-Martín, Andreas Schüller, Marvin Apel, Araceli Gago-Arias

Background: Commercially available ionization chambers (ICs) exposed to ultra-high dose per pulse (UHDP) exhibit deviations from a linear dose response due to volume recombination. Simulation models have been developed to describe the charge collection efficiency (CCE) but focused on parallel-plate ICs. This study aims to measure and simulate the CCE and pol

Haoming He, Zongyang Dan, Zhongqi He, Changjun Liu

In this letter, we present an innovative design for a compact, high-efficiency all-polarization receiving rectenna tailored for wireless power transmission. This rectenna, which integrates an antenna with two same rectifier units, employs direct conjugate matching of antenna impedance to rectifier impedance. This approach eliminates the necessity for an exte

Giorgio Pariani, Guido Agapito, Demetrio Magrin, Matteo Munari

In the framework of the MORFEO project, the Multi-Conjugated Adaptive Optics (MCAO) module for the European Extremely Large Telescope (ELT), we developed an integrated modeling tool to interface the optical model with the adaptive optics simulations, called ASSO (Adaptive opticS Simulation interfaced with Optical model). This tool is our asso nella manica (a

Jacob Taegon Kim, Alex Sim, Kesheng Wu, Jinoh Kim

Monitoring data transfer performance is a crucial task in scientific computing networks. By predicting performance early in the communication phase, potentially sluggish transfers can be identified and selectively monitored, optimizing network usage and overall performance. A key bottleneck to improving the predictive power of machine learning (ML) models in

Aihui Liu, Magnus Jansson

Willems' fundamental lemma uses a key decision variable $g$ to combine measured input-output data and describe trajectories of a linear time-invariant system. In this paper, we ask: what is a good choice for this vector $g$ when the system is affected by noise? For a linear system with Gaussian noise, we show that there exists an optimal subspace for this de

Tushar Singh, Ajim Uddin Ansari, Shiv Datt Kumar

In this paper, we introduce the concept of nonnil-S-Laskerian rings, which generalize both nonnil-Laskerian rings and S-Laskerian rings. A ring R is said to be nonnil-S-Laskerian if every nonnil ideal I (disjoint from S) of R is S-decomposable. As a main result, we prove that the class of nonnil-S-Noetherian rings belongs to the class of nonnil-S-Laskerian r

Imre Barany, Julia Q. Du, Dan Schwarz, Liping Yuan

The Sylvester-Gallai theorem states that for a finite set of points in the plane, if every line determined by any two of these points also contains a third, then the set is necessarily made of collinear points. In this paper, we first provide a counterexample in the plane when the point set is countably infinite but bounded. Then we consider a variant of the

C. Pezzotti, J. Bétrisey, G. Buldgen, M. Gilfanov

In low-mass stars, the connection between magnetic activity, rotation period, and age provides key insights into the functioning of dynamos. Fully understanding the activity-rotation-age relationship requires stars with precise fundamental parameters, measured rotation periods, and reliable magnetic activity indicators (e.g. X-ray luminosity). Thanks to spac

Tomáš Henych, Jiří Borovička, David Čapek, Vlastimil Vojáček

Geminids have the highest bulk density of all major meteor showers and their mechanical strength appears to depend on their mass. They are also the most active annual shower, enabling detailed studies of the dependence of their physical and mechanical properties on mass. We calculated the fragmentation cascades of 39 bright Geminid fireballs, as well as fain

Lin Chen, Yuya Sasaki

This paper studies the identification and estimation of heterogeneous effects of an endogenous treatment under interference and spillovers in a large single-network setting. We model endogenous treatment selection as an equilibrium outcome that explicitly accounts for spillovers and derive conditions guaranteeing the existence and uniqueness of this equilibr

Mahmut S. Gokmen, Mitchell A. Klusty, Peter T. Nelson, Allison M. Neltner

Whole-slide images (WSIs) contain tissue information distributed across multiple magnification levels, yet most self-supervised methods treat these scales as independent views. This separation prevents models from learning representations that remain stable when resolution changes, a key requirement for practical neuropathology workflows. This study introduc

Manuel Bentele, Andreas Podelski, Axel Sikora, Bernd Westphal

Embedded applications often use a Hardware Abstraction Layer (HAL) to access hardware. Improper use of the HAL can lead to incorrect hardware operations, resulting in system failure and potentially serious damage to the hardware. The question is how one can obtain prioritize, among a possibly large set of HAL interface requirements, those that are indisputab

Artemiy Burov, Julien Baglio, Clément Javerzac-Galy

With the latest advances in quantum computing technology, we are gradually moving from the noisy intermediate-scale quantum (NISQ) era characterized by hardware limited in the number of qubits and plagued with quantum noise, to the age of quantum utility where both the newest hardware and software methods allow for tackling problems which have been deemed di

Kezhuo Li, Marco Grzegorczyk

Gaussian Dynamic Bayesian Networks (GDBNs) are a widely used tool for learning network structures from continuous time-series data. To capture both time-lagged and contemporaneous dependencies, advanced GDBNs allow for dynamic inter-slice edges as well as static intra-slice edges. In the literature, two Bayesian modeling approaches have been developed for GD

M. Kaid Slimane, B. N. Brahmi, M. Bouchenaki, S. Bekhechi

The present theoretical study focuses on the structural, electronic and thermoelectric properties of PbTe, PbSe and their ternary alloys PbSe$_{x}$Te$_{1-x}$, using the density functional theory (DFT) by the full potential linearised augmented plane wave (FP-LAPW) method implemented in Wien2k code. Structural properties were performed by using the generalize

Aihui Liu, Magnus Jansson

We propose a fundamental-lemma-free data-driven predictive control (DDPC) scheme for synthesizing model predictive control (MPC)-like policies directly from input-output data. Unlike the well-known DeePC approach and other DDPC methods that rely on Willems' fundamental lemma, our method avoids stacked Hankel representations and the DeePC decision variable g.

Andrei Tokovinin

Inner and outer orbits in twelve hierarchical stellar systems are determined using high-resolution speckle imaging, radial velocities, or both. Masses and fluxes of the components are estimated. The Hipparcos numbers of the main stars are 7111, 12912, 17895, 20375, 42424, 68717, 77439, 79076, 90253, 97922, and 102855; the faint triple WDS J10367+1522 has no

Marianne de Heer Kloots, Paul Boersma, Willem Zuidema

Futrell and Mahowald present a useful framework bridging technology-oriented deep learning systems and explanation-oriented linguistic theories. Unfortunately, the target article's focus on generative text-based LLMs fundamentally limits fruitful interactions with linguistics, as many interesting questions on human language fall outside what is captured by w

Amirhossein Monji, Amirali Modir, Burak Kocuk

We study the Heilbronn triangle problem, which involves placing n points in the unit square such that the minimum area of any triangle formed by these points is maximized. A straightforward maximin formulation of this problem is highly non-linear and non-convex due to the existence of bilinear terms and absolute value equations. We propose two mixed-integer

Emanuele Giorgi, Jonas Wallin

Existing approaches to modelling antibody concentration data are mostly based on finite mixture models that rely on the assumption that individuals can be divided into two distinct groups: seronegative and seropositive. Here, we challenge this dichotomous modelling assumption and propose a latent variable modelling framework in which the immune status of eac

Chao Yi, Dian Chen, Gaoyang Guo, Jiakai Tang

Large language models (LLMs) have demonstrated remarkable potential in transforming recommender systems from implicit behavioral pattern matching to explicit intent reasoning. While RecGPT-V1 successfully pioneered this paradigm by integrating LLM-based reasoning into user interest mining and item tag prediction, it suffers from four fundamental limitations:

F. Serdouk, A. Boumali, M. L. Benkhedir, Y. Goutal

The present study examines the electronic transport characteristics of amorphous semiconductors through TOF measurements and numerical simulations. The primary objective is to determine the DOS in amorphous selenium (a-Se) and to assess the temperature and electric field dependence of the hole mobility. A comprehensive investigation of localized states withi

Emanuel Gallo, Carlos N. Kozameh

This review examines the role of differential forms, Pfaffian systems, and hypersurfaces in general relativity. These mathematical constructions provide the essential tools for general relativity, in which the curvature of spacetime;described by the Einstein field equations;is most elegantly formulated using the Cartan calculus of differential forms. Another

Teodor Poncu, Ioana Pintilie, Marius Dragoi, Dragos Tantaru

Large Language Models (LLMs) typically excel at coding tasks involving high-level programming languages, as opposed to lower-level programming languages, such as assembly. We propose a synthetic data generation method named C-ing Clearly, which leverages the corresponding C code to enhance an LLM's understanding of assembly. By fine-tuning on data generated

Jia Guo, Jiawei Du, Shengzhu Yang, Shuai Lu

Current retinal foundation models remain constrained by curated research datasets that lack authentic clinical context, and require extensive task-specific optimization for each application, limiting their deployment efficiency in low-resource settings. Here, we show that these barriers can be overcome by building clinical native intelligence directly from r

Artem Semidetnov

We introduce and study structured enhancement of the notion of a crossed simplicial group, which we call an operadic crossed simplicial group. We show that with each operadic crossed simplicial group one can associate a certain operad in groupoids. We demonstrate that symmetric and braid crossed simplicial groups can be made into operadic crossed simplicial

B. Vigneshwar, R. Sankaranarayanan

Nonlocality is a defining feature of quantum mechanics and has long served as a key indicator of quantum resources since the formulation of Bell's inequalities. Identifying the contribution of nonlocality to extractable work remains a central problem in quantum thermodynamics. We address this by introducing a quantifier of nonlocal contributions to extractab

Lazlo Bleker, Mustafa Cem Güneş, Pierluigi D'Acunto

Machine learning (ML) is increasingly used in structural engineering and design, yet its broader adoption is hampered by the lack of openly accessible datasets of structural systems. We introduce BridgeNet, a publicly available graph-based dataset of 20,000 form-found bridge structures aimed at enabling Graph ML and multi-modal learning in the context of con

Jianhua Zhang, Jun Liu, Z. Z. Zhu, K. M. Ho

We present a first-principles study of the multimode Jahn-Teller (JT) effect in the exctied $^{3}E$ state of the negatively charged nitrogen-vacancy (NV) center in diamond. Using density functional theory combined with an intrinsic distortion path (IDP) analysis, we resolve the full activation pathways of the JT distortion and quantitatively decompose the di

Ranjini Bhattacharya, Souvik Roy

We theoretically investigate spin-resolved thermoelectric transport in a triangular ladder geometry hosting antiferromagnetic spin alignment, where lattice topology and magnetic ordering jointly enable highly efficient spin-selective energy conversion. The inherent geometric frustration of the ladder, together with intrinsic spin-filtering mechanisms, is sho

K. M. Etmimi, M. A. Ojalah, A. M. Abotruma

First-principles density functional simulations were employed to investigate the geometries, electrical properties, and hyperfine structures of various beryllium-doped diamond configurations, including interstitial (Be$_i$), substitutional (Be$_s$), and beryllium-nitrogen (Be-N) complexes. The incorporation of Be into the diamond lattice is more favorable as

Martin Slawski

The increased prevalence of observational data and the need to integrate information from multiple sources are critical challenges in contemporary data analysis. Record linkage is a widely used tool for combining datasets in the absence of unique identifiers. The presence of linkage errors such as mismatched records, however, often hampers the analysis of da

Cheng-Han Lu, Pei-Hsuan Tsai

Transformer-based multi-modal intelligent systems often suffer from high computational and energy costs due to dense self-attention, limiting their scalability under resource constraints. This paper presents SMMT, a sparse multi-modal transformer architecture designed to improve efficiency and robustness. Building upon a cascaded multi-modal transformer fram