November 2025 arXiv papers — page 176

Rodrigo Bernal, Ignacio Ponce, Federico Milano

This paper proposes a coherency control strategy for Inverter-Based Resources (IBRs) to establish coherence among power system devices. Using the equivalence of the Complex Frequency (CF) of the injected currents as the definition for coherency among devices, the control enforces an output current with a proportional magnitude and a constant phase shift rela

Yong Hu, Christopher Lane, Xiang Chen, Shuting Peng

Many emergent phenomena appear in doped Mott insulators near the insulator-to-metal transition. In high-temperature cuprate superconductors, superconductivity arises when antiferromagnetic (AFM) order is gradually suppressed by carrier doping, and a $\textit{d}$-wave superconducting gap forms when an enigmatic nodal gap evolves into a point node. Here, we ex

Bobae Kim, Regina Caputo, Manoj Jadhav, Sylvester Joosten

AstroPix is a high-voltage CMOS (HV-CMOS) monolithic active pixel sensor originally developed to enable precision gamma-ray imaging and spectroscopy in the medium-energy regime (approximately 100 keV-100 MeV) based on the groundwork laid by ATLASpix and MuPix. It features a 500 um pixel pitch, in-pixel amplification and digitization, and low power consumptio

Benjamin G. Zastrow, Anirban Chaudhuri, Karen E. Willcox, Anthony Ashley

This paper presents a block-structured formulation of Operator Inference as a way to learn structured reduced-order models for multiphysics systems. The approach specifies the governing equation structure for each physics component and the structure of the coupling terms. Once the multiphysics structure is specified, the reduced-order model is learned from s

Jie Zheng, Jiyong Kang, Zheng Zhu, Di Wu

We investigate spin quantum-fluctuation effects that originate from the Heisenberg uncertainty principle during the dynamical cycle of disentanglement, entanglement, and re-disentanglement between itinerant electrons and localized magnetic moments mediated by the s-d exchange interaction. Beyond conventional deterministic spin-transfer torque, we analyze an

David Alfaya, Indranil Biswas, Francois-Xavier Machu

Given a symplectic (respectively, orthogonal) parabolic vector bundle over a compact Riemann surface, we prove that its pullback and direct image through a map between compact Riemann surfaces inherit a natural symplectic (respectively, orthogonal) structure. If the parabolic bundle is endowed with a parabolic Higgs field or a parabolic connection which are

Charlie Dworaczek Guera, Ronan Memin, Michel Pain

In this paper, we are interested in the $\beta$-ensembles (or 1D log-gas) with Freud weights, namely with a potential of the form $|x|^{p}$ with $p \geq 2$. Since this potential is not of class $\mathcal{C}^{3}$ when $p \in (2,3]$, most of the literature does not apply. In this singular setting, we prove a central limit theorem for linear statistics with gen

Giovanni Covi, Ru-Yu Lai, Lili Yan

We study the higher-order fractional Schr\"odinger equation with local nonlinear perturbations and investigate both the forward and inverse problems. We establish both the Sobolev $H^s$ and H\"older $C^s$ estimates for the well-posedness of the nonlinear problem, based on the corresponding estimates derived for the linear fractional Schr\"odinger equation. F

Elinor Thompson, Tiantian He, Anna Schroder, Ahmed Abdulaal

Tractography is a unique method for mapping white matter connections in the brain, but tractography algorithms suffer from an inherent trade-off between sensitivity and specificity that limits accuracy. Incorporating prior knowledge of white matter anatomy is an effective strategy for improving accuracy and has been successful for reducing false positives an

Slim Jmal, Matteo Tacchi-Bénard, Emmanuel Witrant

This paper studies the decay of an objective functional using a new control technique within Pontryagin's framework. Convergence analysis is carried out on the infinite-dimensional space of Tokamak plasma dynamical state as described by weakly decoupled nonlinear partial differential equations. An adjoint-based optimal control is derived to minimize the devi

Kimoi Kemboi, Daniel Krashen, Tianle Liu, Yeqin Liu

Following Krah's method, we construct new examples of phantom categories as semiorthogonal components of the derived categories of two types of rational surfaces: the blowup of the plane at 11 points in general position, and the blowup of the second Hirzebruch surface at 9 points in general position. We also pose conjectures about the existence of phantom su

Anamaría Navarro, E. Khomenko, N. Vitas, T. Felipe

Modeling the solar atmosphere is challenging due to its layered structure and multi-scale dynamics. We aim to validate the new radiative MHD code MAGEC, which combines the MANCHA and MAGNUS codes into a finite-volume, shock-capturing framework, and to test its performance through 2D simulations of magneto-convection. MAGEC is MPI-parallelized and includes im

Eric Godden, Jacquie Groenewegen, Matthew K. X. J. Pan

We present ETHOS (Encountered-Type Haptics for On-demand Social Interaction), a dynamic encountered-type haptic display (ETHD) that enables natural physical contact in virtual reality (VR) during social interactions such as handovers, fist bumps, and high-fives. The system integrates a torque-controlled robotic manipulator with interchangeable passive props

P. Leto, S. Owocki, C. Trigilio, F. Cavallaro

Magnetic BA stars host dipole-like magnetospheres. When detected as radio sources, their luminosities correlate with the magnetic field and rotation. Rotation is crucial because the mechanism undergirding the relativistic electron production is powered by centrifugal breakouts. CBOs occur wherever magnetic tension does not balance centrifugal force; the resu

Zhaolin Li

According to the relative Langlands functoriality conjecture, an admissible morphism between the $L$-groups of spherical varieties should induce a functorial transfer of the corresponding local and global automorphic spectra. Via the relative trace formula approach, two basic problems are the local transfer and the fundamental lemma on the geometric side of

Z. W. Riedel, P. A. E. Murgatroyd, C. S. Kengle, P. M. T. Vianez

The chemical flexibility of the $RM_6X_6$ stoichiometry, where an $f$-block element is intercalated in the CoSn structure type, allows for the tuning of flatbands associated with kagome lattices to the Fermi level and for emergent phenomena due to interactions between the $f$- and $d$-electron lattices. Yet, 5$f$ members of the ``166" compounds are underrepr

Sijie Yang, Jiatong Li, Filip Biljecki

AI has proven highly successful at urban planning analysis -- learning patterns from data to predict future conditions. The next frontier is AI-assisted decision-making: agents that recommend sites, allocate resources, and evaluate trade-offs while reasoning transparently about constraints and stakeholder values. Recent breakthroughs in reasoning AI -- CoT p

Bing-Nan Liu, David A. Lowe, Larus Thorlacius

The quantum induced stress tensor of 3+1-dimensional Einstein gravity, with conformally coupled matter, is studied in an effective field theory approach. In this context, Riegert's non-local effective action is sufficient to reproduce the trace anomaly in curved spacetime but in general the effective action can include additional non-local but scale invarian

Zhixian Liu, Jiahao Sun, Ganyu Xu, Bo Yang

The ability to control solid-state quantum emitters is fundamental to advancing quantum technologies. The performance of these systems is fundamentally governed by their spin-dependent photodynamics, yet conventional control methods using cavities offer limited access to key non-radiative processes. Here we demonstrate that anisotropic lattice strain serves

Wan Chang, Elke-Caroline Aschenauer, Alexander Jentsch, Arjun Kumar

The upcoming Electron-Ion Collider (EIC) will address several outstanding puzzles in modern nuclear physics. Key questions-such as the partonic structure of nucleons and nuclei and the origin of their mass and spin-can be explored through high-energy electron-proton and electron-nucleus collisions. To maximize its scientific reach, the EIC community has advo

Noah Linden, Ronald de Wolf

Quantum phase estimation is one of the most important tools in quantum algorithms. It can be made non-adaptive (meaning all applications of the unitary $U_\phi$ happen simultaneously) without using more applications of $U_\phi$, albeit at the expense of using many more qubits. It is also known that there is no advantage for adaptive algorithms in the case wh

M. de Berg, B. M. P. Jansen, J. S. K. Lamme

The Planar Separator Theorem, which states that any planar graph $\mathcal{G}$ has a separator consisting of $O(\sqrt{n})$ nodes whose removal partitions $\mathcal{G}$ into components of size at most $\tfrac{2n}{3}$, is a widely used tool to obtain fast algorithms on planar graphs. Intersection graphs of disks, which generalize planar graphs, do not admit su

Luozhen Li, Akshay Babu Karyath, Julien Bertrand, Mohsen Falamarzi Askarani

We characterize spectroscopic and coherence properties of the 1451.37 nm excited-state zero-phonon line (ZPL) between the $^{3}F_{4}$ and the $^{3}H_{4}$ manifolds of a thulium-doped yttrium aluminum perovskite (Tm$^{3+}$:YAlO$_3$) crystal at temperatures around 1.5 K. We measure the absorption spectrum between the $^{3}H_{6}$ - $^{3}F_{4}$ and $^{3}F_{4}$ -

Shiyao Xu, Benedetta Liberatori, Gül Varol, Paolo Rota

Recent advances in 3D human motion and language integration have primarily focused on text-to-motion generation, leaving the task of motion understanding relatively unexplored. We introduce Dense Motion Captioning, a novel task that aims to temporally localize and caption actions within 3D human motion sequences. Current datasets fall short in providing deta

Oscar López, Arvind Prasadan, Carlos Llosa-Vite, Richard B. Lehoucq

We establish non-asymptotic efficiency guarantees for tensor decomposition-based inference in count data models. Under a Poisson framework, we consider two related goals: (i) parametric inference, the estimation of the full distributional parameter tensor, and (ii) multiway analysis, the recovery of its canonical polyadic (CP) decomposition factors. Our main

Ilem Leisher, Paul Torrey, Alex M. Garcia, Jonah C. Rose

Dark matter (DM) halos form hierarchically in the Universe through a series of merger events. Cosmological simulations can represent this series of mergers as a graph-like ``tree'' structure. Previous work has shown these merger trees are sensitive to cosmology simulation parameters, but as DM structures, the outstanding question of their sensitivity to DM m

Ramón Nartallo-Kaluarachchi, Renaud Lambiotte, Alain Goriely

We investigate nonequilibrium steady-state dynamics in both continuous- and discrete-state stochastic processes. Our analysis focuses on planar diffusion dynamics and their coarse-grained approximations by discrete-state Markov chains. Using finite-volume approximations, we derive an approximate master equation directly from the underlying diffusion and show

Jürgen Lisenfeld, Alexander K. Händel, Etienne Daum, Benedikt Berlitz

The coherence of superconducting quantum computers is severely limited by material defects that create parasitic two-level-systems (TLS). Progress is complicated by lacking understanding how TLS are created and in which parts of a qubit circuit they are most detrimental. Here, we present a method to determine the individual positions of TLS at the surface of

Alessandro Barletta, Nicholas Marshall, Sara Pollock

In this paper we compare two methods for finding extremal eigenvalues and eigenvectors: the restarted Lanczos method and momentum accelerated power iterations. The convergence of both methods is based on ratios of Chebyshev polynomials evaluated at subdominant and dominant eigenvalues; however, the convergence is not the same. Here we compare the theoretical

Aparna Maya Warrier, Arav Agarwal, Jaromir Savelka, Christopher A Bogart

This research category full paper investigates how community college instructors evaluate interactive, no-code AI literacy resources designed for non-STEM learners. As artificial intelligence becomes increasingly integrated into everyday technologies, AI literacy - the ability to evaluate AI systems, communicate with them, and understand their broader impact

Lucian Trestioreanu, Flaviene Scheidt, Wazen Shbair, Jerome Francois

With the large increase in the adoption of blockchain technologies, their underlying peer-to-peer networks must also scale with the demand. In this context, previous works highlighted the importance of ensuring efficient and resilient communication for the underlying consensus and replication mechanisms. However, they were mainly focused on mainstream, Proof

Maria Huynh, Wilder C. Rodrigues

Historically, bilingualism was often perceived as an additional cognitive load that could hinder linguistic and intellectual development. However, over the last three decades, this view has changed considerably. Numerous studies have aimed to model and understand the architecture of the bilingual word recognition system Dijkstra and van Heuven (2002), invest

Daniel Berio, Michael Stroh, Sylvain Calinon, Frederic Fol Leymarie

We integrate smoothing B-splines into a standard differentiable vector graphics (DiffVG) pipeline through linear mapping, and show how this can be used to generate smooth and arbitrarily long paths within image-based deep learning systems. We take advantage of derivative-based smoothing costs for parametric control of fidelity vs. simplicity tradeoffs, while

Amr Gomaa, Ahmed Salem, Sahar Abdelnabi

As language models evolve into autonomous agents that act and communicate on behalf of users, ensuring safety in multi-agent ecosystems becomes a central challenge. Interactions between personal assistants and external service providers expose a core tension between utility and protection: effective collaboration requires information sharing, yet every excha

Jeff Murugan

We propose a framework for applying on-shell scattering amplitude methods to emergent relativistic phases of quantum matter. Many strongly correlated systems, from Dirac and Weyl semimetals to topological-insulator surfaces, exhibit low-energy excitations that are effectively massless relativistic spinors. We show that physical observables such as nonlinear

Mikhail Tsukerman, Konstantin Grotov, Pavel Ginzburg

We present a conditional diffusion model for electromagnetic inverse design that generates structured media geometries directly from target differential scattering cross-section profiles, bypassing expensive iterative optimization. Our 1D U-Net architecture with Feature-wise Linear Modulation learns to map desired angular scattering patterns to 2x2 dielectri

Manuel Gomes, Bogdan Raducanu, Miguel Oliveira

Articulated object perception presents significant challenges in computer vision, particularly because most existing methods ignore temporal dynamics despite the inherently dynamic nature of such objects. The use of 4D temporal data has not been thoroughly explored in articulated object perception and remains unexamined for panoptic segmentation. The lack of

I. O. Simonenko, R. G. Nazmitdinov, T. N. Vershinina

The fabrication of large, high-quality single crystals (SCs) of all-inorganic cesium lead bromide (CsPbBr$_3$) via accessible methods remains a significant challenge. This work presents a systematic approach to optimize the antisolvent vapor-assisted crystallization (AVC) method, where the experimental design is guided by a theoretical methods at each step.

Jun-ichi Sakamoto, Shinya Tomizawa

Extending the single-angular-momentum case analyzed in our previous work, we investigate the solution-generating technique based on the Breitenlohner-Maison (BM) linear system for asymptotically flat, stationary, bi-axisymmetric black hole solutions with two angular momenta in five-dimensional vacuum Einstein theory. In particular, we construct the monodromy

Carlos Llosa-Vite, Daniel M. Dunlavy, Richard B. Lehoucq, Oscar López

We establish parameter inference for the Poisson canonical polyadic (PCP) model of tensor count data through a latent-variable formulation. Our approach exploits the property that any random tensor that follows the PCP model can be derived by marginalizing an unobservable random tensor of one dimension larger. The loglikelihood of this larger dimensional ten

Sam Patrick, Leonardo Solidoro, Maurício Richartz, Pietro Smaniotto

Draining vortices provide a powerful platform for simulating black hole phenomena in tabletop experiments. In realistic fluid systems confined within a finite container, low-frequency waves amplified by the vortex are reflected at the walls, rendering the system unstable. This process, known in the gravitational context as the black hole bomb, manifests as a

Darinka Dentcheva, Andrzej Ruszczynski

We study differentiability properties of convex operators defined on a Banach space with values in an $\Lc_p$ space and of their compositions with monotonic convex functionals on this space. We develop new tools for operators enjoying an additional feature known as the local property. The new approach and results go beyond the classical theory of normal inte

Shiming Li, Luca Mottola, Yuan Yao, Stefanos Kaxiras

Quantized CNN inference on ultra-low-power MCUs incurs unnecessary computations in neurons that produce saturated output values. These values are too extreme and are eventually clamped to the boundaries allowed by the neuron. Often times, the neuron can save time by only producing a value that is extreme enough to lead to the clamped result, instead of compl

Olaf V. Adan, Dimitra Dritsa, Steven Houben

Collocated collaboration, where individuals work together in the same physical space and time, remains a cornerstone of effective teamwork. However, most collaborative systems are designed to support individual tasks rather than joint activities; they enable interactions for users to complete tasks rather than interactivity to engage in shared experiences. I

H. Olsen, P. Vignolo, M. Albert

We study the interplay of interactions and quasiperiodic driving in the Lieb-Liniger model of one-dimensional bosons subjected to a sequence of delta kicks. Building on the known mapping between the kicked rotor and the Anderson model, we show that both interparticle interactions and quasiperiodic modulations of the kicking strength can independently and sim

Wen Guo, Ya-Guang Wang

In this paper, the well-posedness is studied for the initial boundary value problem of the two-dimensional compressible ideal magnetohydrodynamic (MHD) equations in bounded perfectly conducting domains with corners. The presence of corners yields intrinsic analytic obstacles: the lack of smooth tangential vectors to the boundary prevents the use of classical

D. Kynoch, I. M. McHardy, E. M. Cackett, J. Gelbord

We present results from intensive (x3 daily), three-month-long X-ray, UV and optical monitoring of the bright Seyfert active galactic nucleus (AGN) MCG+08-11-11 with Swift, supported by optical-infrared ground-based monitoring. The 12 resultant, well-sampled, lightcurves are highly correlated; in particular, the X-ray to UV correlation r_max = 0.85 is, as fa

Khedidja Djeha

We revisit the quadratic inflationary potential by introducing a minimal higher-order correction obtained through a simple field redefinition, leading to the potential V(chi) = (1/2) m^2 * (chi - (gamma/14) * chi^7)^2. While the uncorrected quadratic model predicts n_s approximately 0.967 and r approximately 0.13, in strong tension with CMB data, the correct

Nicolas J. Sovare, Walter T. Shmayda

Ionization chamber-based, in-line tritium process monitors play an important part in determining the behavior of a tritium system. The one-liter detection volume monitor has been characterized well through experiment and to respond linearly to tritium concentrations for the range of $1 {\mu}Ci/m^3$ to $1 Ci/m^3$. Additionally, it has been shown to behave non

Xu Zhang, Peng Wang, Chen Wang, Zhe Xu

Strain Gauge Status (SGS) time series recognition is crucial in the field of intelligent manufacturing based on the Internet of Things, as accurate identification helps timely detection of failed mechanical components, avoiding accidents. The loading and unloading sequences generated by strain gauges can be identified through time series classification (TSC)

Lars Grüne, Konrad Kleinberg, Thomas Kruse, Mario Sperl

Neural networks (NNs) have emerged as powerful tools for solving high-dimensional optimal control problems. In particular, their compositional structure has been shown to enable efficient approximation of high-dimensional functions, helping to mitigate the curse of dimensionality in optimal control problems. In this work, we build upon the theoretical framew

Yonghang Ji, Allen Vong

We study repeated task assignment as an instrument for providing effort incentives. Unlike traditional incentive instruments, assignment of a task both determines who produces and provides incentives, and incentives for one worker spill over to others because assignment is exclusive. We show that optimal incentives require a strict and evolving priority rank

Jonas Hänseroth, Aaron Flötotto, Muhammad Nawaz Qaisrani, Christian Dreßler

This work demonstrates that fine-tuning transforms foundational machine-learned interatomic potentials (MLIPs) to achieve consistent, near-ab initio accuracy across diverse architectures. Benchmarking five leading MLIP frameworks (MACE, GRACE, SevenNet, MatterSim, and ORB) across seven chemically diverse compounds reveals that fine-tuning universally enhance

Mario Schwarz, Christoph Vogl, Niko N. P. N. Lay, Tommaso Comellato

Atmosphere-sourced argon contains traces of $^{42}$Ar, whose $\beta^-$-decaying progeny $^{42}$K represents a significant intrinsic background for rare-event experiments using liquid argon (LAr) as detector or shielding medium. Understanding and mitigating this background is crucial for current and future large-scale detectors in neutrino and dark-matter phy

Giovanni Fiaschi, Carlo Vitucci, Thomas Westerbäck, Daniel Sundmark

In graph theory and its practical networking applications, e.g., telecommunications and transportation, the problem of finding paths has particular importance. Selecting paths requires giving scores to the alternative solutions to drive a choice. While previous studies have provided comprehensive evaluation of single-path solutions, the same level of detail

Inmaculada Baldomá, Pau Martín, Donato Scarcella

In this work, we prove that a generic unfolding of an analytic Hamiltonian Hopf singularity (in an open set with codimension 1 boundary) possesses transverse homoclinic orbits for subcritical values of the parameter close to the bifurcation parameter. As a consequence, these systems display chaotic dynamics with arbitrarily large topological entropy. We veri

Jean-Christophe Pain

We present identities for permutations with fixed points. The formulas are based on successive derivations or integrations of the determinant of a particular matrix.

Pablo A. Peña, James S. Jenkins

We present EMPEROR, an open-source Python framework designed for efficient exoplanet detection and characterisation with radial velocities (RV). EMPEROR integrates Dynamic Nested Sampling (DNS) and Adaptive Parallel Tempering (APT) Markov Chain Monte Carlo (MCMC), supporting multiple noise models such as Gaussian Processes (GPs) and Moving Averages (MA). The

Jan-Hendrik Ewering, Robin E. Herrmann, Niklas Wahlström, Thomas B. Schön

Embedding non-restrictive prior knowledge, such as energy conservation laws, into learning methods is a key motive to construct physically consistent dynamics models from limited data, relevant for, e.g., model-based control. Recent work incorporates Hamiltonian dynamics into Gaussian Processes (GPs) to obtain uncertainty-quantifying, energy-consistent model

Robin Ming Chen, Samuel Walsh, Miles H. Wheeler

Hydrodynamic bores are front-type traveling wave solutions to the two-layer free boundary Euler equations in two dimensions. The velocity field in each layer is assumed to be incompressible and irrotational, and it limits to distinct laminar flows upstream and downstream. Rigid horizontal boundaries confine the fluids from above and below. A constant gravita

Sumit Kumar Jana, Ryo Hanai, Tan Van Vu, Hisao Hayakawa

While considering non-Hermitian Hamiltonians arising in the presence of dissipation, in most cases, the dissipation is taken to be frequency independent. However, this idealization may not always be applicable in experimental settings, where dissipation can be frequency-dependent. Such frequency-dependent dissipation leads to non-Markovian behavior. In this

Jakub Woźnicki, Ewelina Zatorska

We study the multi-dimensional Euler-alignment system with a matrix-valued communication kernel, motivated by models of anticipation dynamics in collective behaviour. A key feature of this system is its formal equivalence to a nonlocal variant of the Aw--Rascle--Zhang (ARZ) traffic model, in which the desired velocity is modified by a nonlocal gradient inter

Janet Jenq, Hongda Shen

Multimodal product retrieval systems in e-commerce platforms rely on effectively combining visual and textual signals to improve search relevance and user experience. However, vision-language models such as CLIP are vulnerable to typographic attacks, where misleading or irrelevant text embedded in images skews model predictions. In this work, we propose a no

Firoj Ahmmed Patwary, Abdullah Al Noman

Tokenization is an important first step in Natural Language Processing (NLP) pipelines because it decides how models learn and represent linguistic information. However, current subword tokenizers like SentencePiece or HuggingFace BPE are mostly designed for Latin or multilingual corpora and do not perform well on languages with rich morphology such as Benga

Kilian Lichtner, Pascal Bergsträßer, Moses Ganardi, Anthony W. Lin

Ramsey quantifiers have recently been proposed as a unified framework for handling properties of interests in program verification involving proofs in the form of infinite cliques, which are not expressible in first-order logic. Among others, these include liveness verification and monadic decomposability. We present the tool REAL, which implements an effici

Wanlin Li, Elena Mantovan, Rachel Pries, Yunqing Tang

If $E$ is an elliptic curve, defined over $\mathbb{Q}$ or a number field having at least one real embedding, then Elkies proved that $E$ has supersingular reduction at infinitely many primes $p$. Baba and Granath extended this result to certain curves $C$ of genus $2$ with field of moduli $\mathbb{Q}$, under a condition on the endomorphism ring of the Jacobi

Maximilian Kirschner, Konstantin Dudzik, Ben Krusekamp, Jürgen Becker

Real-time systems, particularly those used in domains like automated driving, are increasingly adopting neural networks. From this trend arises the need for high-performance hardware exhibiting predictable timing behavior. While state-of-the-art real-time hardware often suffers from limited memory and compute resources, modern AI accelerators typically lack

Klára Bendová, Tomáš Knap, Jan Černý, Vojtěch Pour

Criminal justice administrative data contain only a limited amount of information about the committed offense. However, there is an unused source of extensive information in continental European courts' decisions: descriptions of criminal behaviors in verdicts by which offenders are found guilty. In this paper, we study the feasibility of extracting these de

Huanqi Wu, Huangbiao Xu, Runfeng Xie, Jiaxin Cai

Despite remarkable progress in steganography, embedding semantically rich, sentence-level information into carriers remains a challenging problem. In this work, we present a novel concept of Semantic Steganography, which aims to hide semantically meaningful and structured content, such as sentences or paragraphs, in cover media. Based on this concept, we pre

Chen Tang, Bohang Nan, Xiaodong Liu, Guiying Xu

To achieve high performance thermoelectric materials and devices, thermoelectric transistors, which integrate thermoelectric effects with transistor technology, represent a promising approach. Here p type Bi0.5Sb1.5Te3 and n type Bi2Te2.97Se0.03 are used as the constituent materials for an NPN transistor. By applying forward bias to the emitter and reverse b

Hui Tong, Thomas A. Callister, Maya Fishbach, Eric Thrane

Gravitational-wave observations of massive, rapidly spinning binary black holes mergers provide increasing evidence for the dynamical origin of some mergers. Previous studies have interpreted the mergers with primary mass $\gtrsim45\,M_\odot$ as being dominated by hierarchical, second-generation mergers, with rapidly spinning primaries being the products of

Nourhaine Nefzi, Abir Abid

The aim of this paper is to dig deeper into understanding the exchange rates and uncertainty dependence. Using the novel Baker et al. (2020)'s daily Twitter Uncertainty Index and BRICS exchange rates, we investigate their extreme tail dependence within an original time-varying copula framework. Our analysis makes several noteworthy results. Evidence for Indi

B. Liagre, A. Desai, L. Einramhof, L. Bugnet

Dipolar (l=1) mixed modes revealed surprisingly weak differential rotation between the core and the envelope of evolved solar-like stars. Quadrupolar (l=2) mixed modes also contain information on the internal dynamics, but are very rarely characterised due to their low amplitude and the challenging identification of adjacent or overlapping rotationally split

Marvin Fritz, Nikos I. Kavallaris

We study a time-fractional Fisher-KPP equation involving a Riemann-Liouville fractional derivative acting on the diffusion term, as derived by Angstmann and Henry (Entropy, 22:1035, 2020). The model captures memory effects in diffusive population dynamics and serves as a framework for tumor growth modeling. We first establish local well-posedness of weak sol

Valeriu Dimidov, Faisal Hawlader, Sasan Jafarnejad, Raphaël Frank

Economic constraints, limited availability of datasets for reproducibility and shortages of specialized expertise have long been recognized as key challenges to the adoption and advancement of predictive maintenance (PdM) in the automotive sector. Recent progress in large language models (LLMs) presents an opportunity to overcome these barriers and speed up

Shreya Gupta, Ojasva Saxena, Arghodeep Nandi, Sarah Masud

Podcasts have become a central arena for shaping public opinion, making them a vital source for understanding contemporary discourse. Their typically unscripted, multi-themed, and conversational style offers a rich but complex form of data. To analyze how podcasts persuade and inform, we must examine their narrative structures -- specifically, the narrative

Xu Zhang, Zhengang Huang, Yunzhi Wu, Xun Lu

Time series forecasting is important in finance domain. Financial time series (TS) patterns are influenced by both short-term public opinions and medium-/long-term policy and market trends. Hence, processing multi-period inputs becomes crucial for accurate financial time series forecasting (TSF). However, current TSF models either use only single-period inpu

Gaganpreet Singh, Rustam Balafendiev, Mahesh Singh Bist, Thomas J. L. J. Gascard

We present results of an extensive suite of numerical simulations that probe square-tiled microwave absorber performance as a function of material properties, frequency, geometry, and unit cell size. The work, which probes both specular reflection and total absorption, highlights the critical importance of the absorber scale size relative to the incidence wa

Matteo Bastico, David Ryckelynck, Laurent Corté, Yannick Tillier

As 3D point clouds become a cornerstone of modern technology, the need for sophisticated generative models and reliable evaluation metrics has grown exponentially. In this work, we first expose that some commonly used metrics for evaluating generated point clouds, particularly those based on Chamfer Distance (CD), lack robustness against defects and fail to

Akua K. Dickson, Juan C. Pacheco Garcia, Andrew P. Sabelhaus

Soft robot manipulators have the potential for deployment in delicate environments to perform complex manipulation tasks. However, existing obstacle detection and avoidance methods do not consider limits on the forces that manipulators may exert upon contact with delicate obstacles. This work introduces a framework that maps force safety criteria from task s

E. F. Sheka

This review presents the covalent chemistry of carbon within the spin-radical concept of electron interaction. Using the language of valence bond trimodality, the regions of classical spinless covalence and its spin counterpart are defined. Carbon is the only element exhibiting spin covalent chemistry. Classical covalent chemistry of carbon concerns molecula

Akhil Ajikumar, Sahil Mayenkar, Steven Yoo, Sakib Reza

Extended reality (XR) research increasingly relies on the ability to stream and synchronize multimodal data between headsets and immersive applications for data-driven interaction and experimentation. However, developers face a critical gap: the Platform for Situated Intelligence (psi), which excels at deterministic temporal alignment and multimodal data man

Zsolt Gulacsi

Exact results are presented for itinerant systems demonstrating that the nearest neighbor Coulomb repulsion (V) destroys the homogeneity in the high concentration regime, this property being not present in the low concentration domain. Since the effects of V often seems contradictory, and the number of phases in which it could appear is extremely large, this

Qianru Meng, Xiao Zhang, Zhaochen Ren, Joost Visser

Code review generation can reduce developer effort by producing concise, reviewer-style feedback for a given code snippet or code change. However, generation-only models often produce generic or off-point reviews, while retrieval-only methods struggle to adapt well to new contexts. In this paper, we view retrieval augmentation for code review as retrieval-au

Arthur Satouf, Yuxuan Zong, Habiboulaye Amadou-Boubacar, Pablo Piantanida

Generative retrieval (GR) differs from the traditional index-then-retrieve pipeline by storing relevance in model parameters and generating retrieval cues directly from the query, but it can be brittle out of domain and expensive to scale. We introduce QueStER (QUEry SpecificaTion for gEnerative Keyword-Based Retrieval), which bridges GR and query reformulat

Emmanuel Pio Pastore, Giuseppe Passarino, Peppino Sapia, Francesco De Rango

Shannon entropy is widely used to measure the complexity of DNA sequences but suffers from saturation effects that limit its discriminative power for long uniform segments. We introduce a novel metric, the entropy rank ratio R, which positions a target sequence within the full distribution of all possible sequences of the same length by computing the proport

Zhenyu Yang, Kairui Zhang, Yuhang Hu, Bing Wang

Despite significant progress in Video Large Language Models (Video-LLMs) for offline video understanding, existing online Video-LLMs typically struggle to simultaneously process continuous frame-by-frame inputs and determine optimal response timing, often compromising real-time responsiveness and narrative coherence. To address these limitations, we introduc

Emiel Vanspranghels, Raquel Marina Noguera Oishi, Franco Minucci, Sofie Pollin

The evolution of mobile networks towards user-centric cell-free distributed Massive MIMO configurations requires the development of novel signal processing techniques. More specifically, digital precoding algorithms have to be designed or adopted to enable distributed operation. Future deployments are expected to improve coexistence between cellular generati

Jean Alexandre, Lucien Heurtier, Silvia Pla

We present a non-perturbative framework for the dynamics of slow-roll inflation that consistently incorporates quantum corrections, based on an alternative functional renormalisation group (RG) approach. We derive the coupled Friedmann-RG flow equations governing the joint evolution of spacetime, the inflaton field, and its effective potential. Applying this

Jon Kleinberg, Fan Wei

The success of large language models (LLMs) has motivated formal theories of language generation and learning. We study the framework of \emph{language generation in the limit}, where an adversary enumerates strings from an unknown language $K$ drawn from a countable class, and an algorithm must generate unseen strings from $K$. Prior work showed that genera

Rui Yan, Yibo Li, Han Ding, Fei Wang

Electroencephalogram (EEG)-based emotion recognition is vital for affective computing but faces challenges in feature utilization and cross-domain generalization. This work introduces EmotionCLIP, which reformulates recognition as an EEG-text matching task within the CLIP framework. A tailored backbone, SST-LegoViT, captures spatial, spectral, and temporal f

Jiaxi Yin, Pengcheng Wang, Han Ding, Fei Wang

Accurate food intake detection is vital for dietary monitoring and chronic disease prevention. Traditional self-report methods are prone to recall bias, while camera-based approaches raise concerns about privacy. Furthermore, existing wearable-based methods primarily focus on a limited number of food types, such as hamburgers and pizza, failing to address th

Giancarlo Bigi, Davide Fioriti, Antonio Frangioni, Mauro Passacantando

An energy community is modeled as a cooperative game, where a veto player is needed beyond the prosumers to manage the community, and the worth of a coalition is its benefit compared to the selfish behaviour of the prosumers. Properties of the game such as superadditivity, monotonicity, convexity and balancedness are analyzed both in the presence and absence

Tommy Mordo, Omer Madmon, Moshe Tennenholtz

In this paper, we study cooperation in distributed games under network-constrained communication. Building on the framework of Monderer and Tennenholtz (1999), we derive a sufficient condition for cooperative equilibrium in settings where communication between agents is delayed by the underlying network topology. Each player deploys an agent at every locatio

Marius Fracarolli, Michael Staniek, Stefan Riezler

Balancing strong privacy guarantees with high predictive performance is critical for time series forecasting (TSF) tasks involving Electronic Health Records (EHR). In this study, we explore how data augmentation can mitigate Membership Inference Attacks (MIA) on TSF models. We show that retraining with synthetic data can substantially reduce the effectivenes

Karim Alexander Adiprasito, Ryoshun Oba, Stavros Argyrios Papadakis, Vasiliki Petrotou

We prove, in any positive characteristic, Parseval-Rayleigh identities for the residue map of a homogeneous complete intersection. As an application, we give a conceptual proof of the folklore fact that generic homogeneous complete intersections have the Strong Lefschetz Property over any field of characteristic 2.

Nicolae Ciprian Bonciocat

To study a Dirichlet polynomial $f(s)=\frac{a_{m}}{m^{s}}+\cdots +\frac{a_{n}}{n^{s}}$ by regarding it as a multivariate polynomial via the canonical map $\phi$ sending $p_i^{-s}$ to an indeterminate $X_i$, with $p_i$ the $i$th prime number, requires knowing the prime factorizations of all the integers in the support of $f$. We devise several methods to stud

Teqi Hao, Xioayu Tan, Shaojie Shi, Yinghui Xu

The personalization of black-box large language models (LLMs) is a critical yet challenging task. Existing approaches predominantly rely on context injection, where user history is embedded into the prompt to directly guide the generation process. However, this single-step paradigm imposes a dual burden on the model: generating accurate content while simulta

Kenny Bešter Štorgel, Clément Dallard, Vadim Lozin, Martin Milanič

For a graph $G$, we denote by $\alpha(G)$ the size of a maximum independent set and by $\omega(G)$ the size of a maximum clique in $G$. Our paper lies on the edge of two lines of research, related to $\alpha$ and $\omega$, respectively. One of them studies $\alpha$-variants of graph parameters, such as $\alpha$-treewidth or $\alpha$-degeneracy. The second li

Julian Saffer

The IceCube Observatory comprises a cubic-kilometer particle detector deep in the Antarctic ice and the cosmic-ray air-shower array IceTop at the surface above. Previous analyses of the cosmic-ray composition have used coincident events with IceTop detecting the electromagnetic shower footprint as well as GeV muons, while the sensors submerged in the ice mea