October 2025 arXiv papers — page 138

Rahul Verma, Yash Vardhan, Hsin Lin, Bahadur Singh

Quantum spin Hall (QSH) insulators are versatile platforms for exploring exotic quantum phases, especially when combined with high-order van Hove singularities (VHSs) that enhance electron correlations. However, perfect spin Hall quantization is often hindered by spin mixing from strong spin-orbit coupling, and the emergence of such VHSs is highly sensitive

Alex Gower

Physical systems that naturally perform energy descent offer a direct route to accelerating machine learning. Oscillator Ising Machines (OIMs) exemplify this idea: their GHz-frequency dynamics mirror both the optimization of energy-based models (EBMs) and gradient descent on loss landscapes, while intrinsic noise corresponds to Langevin dynamics - supporting

Zach Hunter, Matthew Kwan, Lisa Sauermann, Mehtaab Sawhney

Let $1\le k\le n$ and $M$ be a random $n\times n$ matrix with independent uniformly random $\{\pm 1\}$-entries. We show that there exists an absolute constant $c > 0$ such that \[\mathbf{P}[\operatorname{rank}(M)\le n-k]\le \exp(-c nk).\]

Simon Stevenson, Anais Möller, Jade Powell

Since the observation of the binary neutron star merger GW170817 and the associated kilonova AT2017gfo, the next joint gravitational-wave/optical kilonova has been highly anticipated. Overlapping observations between the Vera C. Rubin Observatory and the international gravitational-wave detector network are expected soon. Wide-field survey facilities, such a

Sanghyun Byun, Jung Ick Guack, Mohanad Odema, Baisub Lee

Vision-language models (VLMs) achieve remarkable performance through large-scale image-text pretraining. However, their reliance on labeled image datasets limits scalability and leaves vast amounts of unlabeled image data underutilized. To address this, we propose Unified Vision-Language Alignment for Zero-Label Enhancement (ViZer), an enhancement training f

Cory Hilton, Mohammad Rashid, Faiz Sherman, Steven Bush

We present an approach to identifying wireless microwave tags using radio frequency (RF) fingerprinting and machine learning. The tags are designed for low cost and simplicity, consisting of only two antennas and a single nonlinear element (a diode). An interrogating transceiver transmits a signal consisting of a set of individual frequency tones that is cap

Pierre Berger

We survey a few results on differentiable, symplectic, or analytic wild dynamics.

Julian Minder, Clément Dumas, Stewart Slocum, Helena Casademunt

Finetuning on narrow domains has become an essential tool to adapt Large Language Models (LLMs) to specific tasks and to create models with known unusual properties that are useful for research. We show that narrow finetuning creates strong biases in LLM activations that can be interpreted to understand the finetuning domain. These biases can be discovered u

Haolin Li, Hoda Bidkhori

We introduce a novel framework for Federated Class Incremental Learning, called Federated Gaussian Task Embedding and Alignment (FedGTEA). FedGTEA is designed to capture task-specific knowledge and model uncertainty in a scalable and communication-efficient manner. At the client side, the Cardinality-Agnostic Task Encoder (CATE) produces Gaussian-distributed

Jaron Kent-Dobias

The study of random landscapes has long relied on counting stationary points: metastable states and the barriers between them. However, this method is useless for describing flat regions, common in constraint satisfaction problems. We introduce a characterization of flat regions by counting the number of spheres that can be uniquely inserted into them, eithe

Nil-Jana Akpinar, Chia-Jung Lee, Vanessa Murdock, Pietro Perona

Large Language Models (LLMs) should answer factual questions truthfully, grounded in objective knowledge, regardless of user context such as self-disclosed personal information, or system personalization. In this paper, we present the first systematic evaluation of LLM robustness to inquiry personas, i.e. user profiles that convey attributes like identity, e

Pavel Pochobradský, Ondřej Procházka, Robert Pěnička, Vojtěch Vonásek

In this letter, we introduce Geometric Model Predictive Path Integral (GMPPI), a sampling-based controller capable of tracking agile trajectories while avoiding obstacles. In each iteration, GMPPI generates a large number of candidate rollout trajectories and then averages them to create a nominal control to be followed by the controlled Unmanned Aerial Vehi

Andreas Leibetseder, Klaus Schoeffmann, Jörg Keckstein, Simon Keckstein

Endometriosis is a common women's condition exhibiting a manifold visual appearance in various body-internal locations. Having such properties makes its identification very difficult and error-prone, at least for laymen and non-specialized medical practitioners. In an attempt to provide assistance to gynecologic physicians treating endometriosis, this demo p

M. M. Chernin, A. Yu. Konyaev

In this work, we solve the fundamental problem of describing the coordinate transformations that preserve the upper triangular Toeplitz form of the given operator field. Surprisingly, this problem is closely related to the description of all Nijenhuis operators in the same form. This description, as well as the formulas for the aforementioned coordinate tran

Kohei Hayashi, Stefano Olla

We study the fluctuations of the phonon modes in a one-dimensional chain of anharmonic oscillators where the deterministic Hamiltonian dynamics is perturbed by random exchanges of momentum between nearest neighbor particles. There are three locally conserved quantities: volume, momentum and energy. We study the evolution in equilibrium of the fluctuation fie

Donald Richards

This article represents a personal tribute to Richard Askey together with a new look at some of his favorite integrals, including the Cauchy beta integral. The article also provides some new multidimensional extensions of Cauchy's beta integral in which the domain of integration is the space of real symmetric matrices, and these multidimensional integrals ar

Justin Z. Tam, Pascal Grosset, Divya Banesh, Nesar Ramachandra

Analyzing large-scale scientific datasets presents substantial challenges due to their sheer volume, structural complexity, and the need for specialized domain knowledge. Automation tools, such as PandasAI, typically require full data ingestion and lack context of the full data structure, making them impractical as intelligent data analysis assistants for da

Mouhyemen Khan, Tatsuya Ibuki, Abhijit Chatterjee

Level set methods underpin modern safety techniques such as control barrier functions (CBFs), while also serving as implicit surface representations for geometric shapes via distance fields. Inspired by these two paradigms, we propose a unified framework where the implicit surface itself acts as a CBF. We leverage Gaussian process (GP) implicit surface (GPIS

Ian Jauslin, Vieri Mastropietro

We consider a lattice model of twisted bilayer graphene (TBG) for incommensurate twist angles, focusing on the role of large-momentum-transfer Umklapp terms. These terms, which nearly connect the Fermi points of different layers, are typically neglected in effective continuum descriptions but could, in principle, destroy the Dirac cones; they are indeed clos

Aiden Gundersen, Neil J. Cornish

Neal's funnel refers to an exponential tapering in probability densities common to Bayesian hierarchical models. Usual sampling methods, such as Markov Chain Monte Carlo, struggle to efficiently sample the funnel. Reparameterizing the model or analytically marginalizing local parameters are common techniques to remedy sampling pathologies in distributions ex

Giosue Migliorini, Padhraic Smyth

Systems of interacting continuous-time Markov chains are a powerful model class, but inference is typically intractable in high dimensional settings. Auxiliary information, such as noisy observations, is typically only available at discrete times, and incorporating it via a Doob's $h$-transform gives rise to an intractable posterior process that requires app

Marisa C. Peczuh, Nischal Ashok Kumar, Ryan Baker, Blair Lehman

As the world becomes increasingly saturated with AI-generated content, disinformation, and algorithmic persuasion, critical thinking - the capacity to evaluate evidence, detect unreliable claims, and exercise independent judgment - is becoming a defining human skill. Developing critical thinking skills through timely assessment and feedback is crucial; howev

Zhi Liu, Chengxi Liu, Jiangbei Han, Rui Qiu

This paper proposes a wideband composite sequence impedance model (WCSIM)-based analysis method to evaluate the interactions in power-electronic-based power systems subjected to unbalanced grid faults or with unbalanced loads. The WCSIM-based method intuitively assesses the impact of the small-signal interconnection among the positive-, negative-, and zero-s

Fabrizio Patuzzo

We present a modern reconstruction of the classical formula, first derived by medieval Arab astronomers, that describes the trajectory of the tip of a gnomon's shadow during the day as a function of latitude, solar declination, and gnomon height. Unlike the traditional derivations based on spherical trigonometry, our approach uses only elementary vector alge

Abdelali Arous, Hamza Haif, Huseyin Arslan

Integrated sensing and communication (ISAC) in monostatic in-band full-duplex (IBFD) systems encounters significant challenges due to self-interference (SI) at the radar receiver during concurrent communication and radar operations. This paper proposes a novel waveform-domain self-interference cancellation (SIC) technique that leverages the unique properties

Yasin Simsek

Betas from spot regressions are central to asset pricing and risk management, as measures of systematic risk. This paper develops a new estimation and inference framework for spot regressions by leveraging high-frequency candlesticks, extending conventional (open-to-close) returns with intra-period high/low prices. Specifically, I construct candlestick-based

Neda Abdollahpour, N. Sertac Artan, Ian Daly, Mohammadreza Yazdchi

Analyzing neural data such as Electroencephalography (EEG) data often involves dealing with high-dimensional datasets, where not all channels provide equally meaningful informa- tion. Selecting the most relevant channels is crucial for improving computational efficiency and ensuring robust insights into neural dynamics. This study introduces the Importance o

Takafumi Nogami, Satoshi Kagiwada, Hitoshi Iyatomi

Various deep learning-based systems have been proposed for accurate and convenient plant disease diagnosis, achieving impressive performance. However, recent studies show that these systems often fail to maintain diagnostic accuracy on images captured under different conditions from the training environment -- an essential criterion for model robustness. Man

Rouzbeh Behnia, Jeremiah Birrell, Arman Riasi, Reza Ebrahimi

Federated learning (FL) enables organizations to collaboratively train models without sharing their datasets. Despite this advantage, recent studies show that both client updates and the global model can leak private information, limiting adoption in sensitive domains such as healthcare. Local differential privacy (LDP) offers strong protection by letting ea

Tarik Aougab

In this chapter, we outline some of the many combinatorial tools developed over the past three decades for studying a pseudo-Anosov diffeomorphism of a surface by analyzing the geometry of its mapping torus. We begin with an overview of the various simplicial complexes associated with a surface (such as the curve, arc, and pants complexes) and explain how to

Luke Hetzel

We answer two questions of Kra, Moreira, Richter and Robertson regarding the existence of infinite sumsets of the form $B + C$ in dense and sparse sets of integers and the relation of sumsets to sets of recurrence. We then further generalize these results, yielding new characterizations of sets of multiple measurable and topological recurrence.

Serban Matei Mihalache, Tomoro Mochida

We study polygon equations and their connections to simplex equations, which generalize the pentagon and Yang-Baxter equations, respectively. First, we show that certain ''commutative'' pairs of solutions of (dual) polygon equations give rise to solutions of higher-order polygon equations. Next, we define an explicit compatibility condition b

Saurav Sharma, Chinedu Innocent Nwoye, Didier Mutter, Nicolas Padoy

Surgical future prediction, driven by real-time AI analysis of surgical video, is critical for operating room safety and efficiency. It provides actionable insights into upcoming events, their timing, and risks-enabling better resource allocation, timely instrument readiness, and early warnings for complications (e.g., bleeding, bile duct injury). Despite th

M. Malečková, I. Zymak, A. Spesyvyi, M. Polášek

Cosmic and planetary dust hold vital clues to the chemical evolution of the solar system, yet in situ analysis of their molecular and elemental composition remains technically challenging. Here we present laboratory results from HANKA (High-resolution mass Analyzer for Nano-scale Kinetic Astro materials), a compact Orbitrap-based mass spectrometer developed

Henrik Shahgholian

This note presents reflections drawn from my recent experiences in teaching a course on mathematics and sustainability, with a particular emphasis on raising awareness of the topic and its broader implications. The lectures were structured to bridge conceptual understanding with practical engagement--combining mathematical modeling, critical discussion, and

Haithem Turki, Qi Wu, Xin Kang, Janick Martinez Esturo

Rigorous testing of autonomous robots, such as self-driving vehicles, is essential to ensure their safety in real-world deployments. This requires building high-fidelity simulators to test scenarios beyond those that can be safely or exhaustively collected in the real-world. Existing neural rendering methods based on NeRF and 3DGS hold promise but suffer fro

T. Gökalp Elaçmaz, Ivan Martinez-Soler, Yuber F. Perez-Gonzalez

We investigate constraints on large extra dimensions (LED) using the latest results from reactor antineutrino experiments. Specifically, we analyze the full data sets from Daya Bay, RENO, KamLAND, NEOS, and STEREO to derive updated bounds. For the case of one extra dimension, we find constrains on its radius $a$ of $a \lesssim 0.58~{\rm \mu m}$ ($a \lesssim

Shouang Wei, Min Zhang, Xin Lin, Bo Jiang

Recently, several multi-turn dialogue benchmarks have been proposed to evaluate the conversational abilities of large language models (LLMs). As LLMs are increasingly recognized as a key technology for advancing intelligent education, owing to their ability to deeply understand instructional contexts and provide personalized guidance, the construction of ded

H. J. H. Brouwers

In this paper the excluded volume of binary similar hyperparticles with small size difference in D-dimensional Euclidean spaces R2, R3, and R4 is studied using two different statistical geometry approaches. These geometric approaches, concerning orientation geometry and integral geometry, yield the excluded volume of particle pairs. The excluded volume of re

Sanjay Johnson, Dirk Lauinger, Sungho Shin, François Pacaud

As GPU-accelerated mathematical programming techniques mature, there is growing interest in utilizing them to address the computational challenges of power system optimization. This paper introduces ExaModelsPower.jl, an open-source modeling library for creating GPU-compatible nonlinear AC optimal power flow models. Built on ExaModels.jl, ExaModelsPower.jl p

Ekram Hossain, Angelo Vera-Rivera

Cellular wireless networks enable mobile broadband connectivity for Internet-based applications through their radio access and core network infrastructure. While Fifth-Generation (5G) cellular systems are currently being deployed, ongoing research on cellular technologies primarily focuses on Sixth-Generation (6G) networks to set the stage for developing sta

Marguerite Epstein-Martin, Nicholas Stone, Juliette Becker

Mean motion resonances (MMRs) are a generic outcome of convergent migration for bodies embedded in accretion disks around a central mass. Long studied in planetary systems, the same phenomenon should occur for stellar-mass black holes (BHs) in AGN disks. In this work, we derive simple analytic criteria describing when BH pairs are driven out of resonance, an

Nihar Gargava, Vlad Serban, Maryna Viazovska, Ilaria Viglino

We study the shortest vector lengths in module lattices over arbitrary number fields, with an emphasis on cyclotomic fields. In particular, we sharpen the techniques of arXiv:2308.15275v2 to establish improved results for the variance of the number of lattice vectors of bounded Euclidean norm in a random module lattice. We then derive tight probabilistic bou

Jennifer Wallace, Taevis Kolz, Cara Battersby, Aleksandra Kuznetsova

Investigating the multi-scale fragmentation of dense clumps into compact cores is essential for understanding the processes that govern the initial distribution of mass in stellar clusters and how high-mass stars ($>8~M_{\odot}$) form. We present a catalog of the hierarchical continuum structure from 904 clumps observed in the ALMAGAL program, a high resolut

Dac-Nhan-Tam Nguyen

We give a precise, computable formula for comparing $\lambda$-invariants between modular forms in the anticyclotomic indefinite setting where the Selmer groups have positive rank. This is an improvement of Hatley-Lei \cite{HL19, HL21} where the authors give a formula with incomputable error terms.

Wei Da, Evangelia Kalyvianaki

This paper presents Dodoor, a randomized decentralized scheduler for heterogeneous clusters. Dodoor removes hot-path probing via batched cache refreshes and introduces a heterogeneity-aware resource-load score that ranks sampled candidates using multidimensional fit and queued-duration pressure. On a 101-node CloudLab cluster, Dodoor cuts scheduler messages

Yunxing Li, Peigen Li, Taimin Miao, Rui Xu

The newly discovered kagome superconductor CsCr3Sb5 exhibits distinct features with flat bands and unique magnetism, providing a compelling platform for exploring novel quantum states of correlated electron systems. Emergent charge order in this material is a key for understanding unconventional superconductivity, but it remains unexplored at the atomic scal

Carlos Vieira, Carlos de Gois, Pedro Lauand, Lucas E. A. Porto

A central aspect of quantum information is that correlations between spacelike separated observers sharing entangled states cannot be reproduced by local hidden variable (LHV) models, a phenomenon known as Bell nonlocality. If one wishes to explain such correlations by classical means, a natural possibility is to allow communication between the parties. In p

Laurent Lellouch, Alessandro Lupo, Mattias Sjö, Kálmán Szabo

Hadronic vacuum polarization at low virtualities limits the precision of experimental tests of the standard model via important physical observables. Here we compute that effect in two-flavor chiral perturbation theory to three loops. Among the master integrals that describe the amplitude, six are elliptic functions of the momentum. Of these five are new to

Jessica Fintzen

The building blocks for irreducible smooth representations of p-adic groups are the supercuspidal representations. In these notes that are an expansion of a lecture series given during the IHES summer school 2022 we will explore an explicit exhaustive construction of these supercuspidal representations and their character formulas and observe a striking para

Thede de Boer, Jisuke Kubo, Manfred Lindner, Markus Reinig

We propose a mechanism where the dynamical generation of the Planck mass in scale invariant gravity leads to Einstein gravity, successful inflation and an explanation of the hierarchy problem of the Standard Model. We will discuss the scale generation by dynamical symmetry breaking and phenomenological consequences.

José Blanco, Víctor H. Cárdenas, Cuauhtémoc Campuzano

We present a model--independent reconstruction of the normalized dark energy density function, $X(z) \equiv \rho_{\mathrm{de}}(z)/\rho_{\mathrm{de}}(0)$, derived directly from the DES-SN5YR Type~Ia supernova sample. The analysis employs an inversion formalism that relates the derivative of the distance modulus, $\mu^{\prime}(z)$, to the expansion history, al

Thomas W. Grimm, Damian van de Heisteeg, Filippo Revello

We study the cosmology of axion-scalar pairs, coupled by a hyperbolic field-space metric and with a string-motivated rational scalar potential. Borrowing tools from the theory of dynamical systems, we are able to classify all late-time trajectories and extract physical properties of the asymptotic solutions. These results suggest a Dynamical Distance Conject

Siting Tang, Francesco Albarelli, Yue Zhang, Shunlong Luo

The statistical complexity of continuous-variable quantum states can be characterized with a quantifier defined in terms of information-theoretic quantities derived from the Husimi Q-function. In this work, we utilize this complexity quantifier of quantum states to study the complexity of single-mode bosonic quantum channels. We define the complexity of quan

Abdelaziz Hussein, Gonzalo Herrera

We investigate the possibility that cosmic-ray electron cooling through dark matter-electron scatterings contributes to the low radiative efficiency observed in radio-loud galaxies such as M87. Light dark matter can scatter efficiently off electrons in M87, lowering the observed bolometric luminosity compared to astrophysical expectations. This consideration

Julien Pinske, Klaus Mølmer

In quantum censorship, an agency oversees quantum communication in a public-domain network. The agency restricts the users communication to the free states of a quantum resource theory (QRT). Despite quantum correlations being fragile, any realistic censorship leaves behind some quantumness, raising concerns that censorship may be overcome through revival or

Andrew Hallam, Matthew Yusuf, Aashish A. Clerk, Ivar Martin

Symmetry plays a fundamental role in many-body systems, both in and out of equilibrium. The quantum Mpemba effect (QME) - a phenomenon where systems initially farther from equilibrium can thermalize faster - can be understood in terms of how rapidly a symmetry, broken by initial conditions, is dynamically restored. In this work, we study the QME in a one-dim

Obada Nairat, John F. Beacom, Shirley Weishi Li

While the potential of the Jiangmen Underground Neutrino Observatory (JUNO) to measure solar neutrinos is known, realizing this potential requires new techniques to reduce detector backgrounds. One of the most serious backgrounds is due to the beta decays of unstable nuclei produced through muon breakup (spallation) of nuclei. This background is much more si

Hancheng Ye, Zhengqi Gao, Mingyuan Ma, Qinsi Wang

Multi-agent large language model (LLM) systems are increasingly adopted for complex language processing tasks that require communication and coordination among agents. However, these systems often suffer substantial overhead from repeated reprocessing of overlapping contexts across agents. In typical pipelines, once an agent receives a message from its prede

Yoram Lithwick, Eugene Chiang, Leon Mikulinsky, Zhenbang Yu

Can a disk orbiting a central body be eccentric, when the disk feels its own self-gravity and is pressureless? Contradictory answers appear in the literature. We show that such a disk can be eccentric, but only if it has a sharply truncated edge: the surface density $\Sigma$ must vanish at the edge, and the $\Sigma$ profile must be sufficiently steep at the

Luciano Rezzolla, Christian Ecker

The stellar compactness, that is, the dimensionless ratio between the mass and radius of a compact star, $\mathcal{C} := M/R$, plays a fundamental role in characterising the gravitational and nuclear-physics aspects of neutron stars. Yet, because the compactness depends sensitively on the unknown equation of state (EOS) of nuclear matter, the simple question

Roberto Emparan, Pierre Heidmann

We construct a new family of type IIB supergravity solutions corresponding to states of the D1-D5-P-KKm system that carry the same charges and energy as the non-extremal four-charge black hole and are asymptotic to AdS$_3 \times ($S$^3/\mathbb{Z}_{N_k}) \times$ T$^4$. The solutions consist of static binaries of two extremal D1-D5-P black holes with S$^3$ hor

Kartik Narayan, Yang Xu, Tian Cao, Kavya Nerella

Multimodal Large Language Models (MLLMs) in real-world applications require access to external knowledge sources and must remain responsive to the dynamic and ever-changing real-world information in order to address information-seeking and knowledge-intensive user queries. Existing approaches, such as retrieval augmented generation (RAG) methods, search agen

Lukas Homeier, Andrea Pizzi, Hongzheng Zhao, Jad C. Halimeh

Universal aspects of thermalization in interacting many-body systems are challenging to derive microscopically, especially in kinetically constrained models, yet their numerical study beyond $(1+1)$D remains notoriously difficult. Here, we numerically study the mean-field dynamics of a $(2+1)$D spin system with thousands of spins and show that experimentally

Falk Hassler, Yuho Sakatani

We present a systematic framework for constructing consistent truncations of supergravity based on exceptional generalized cosets of the form $\GS \backslash G/H$. This approach generalizes the well-established generalized Scherk-Schwarz reductions on generalized parallelizable spaces $G/H$, which preserve maximal supersymmetry, to scenarios with reduced sup

Qing Jiang, Junan Huo, Xingyu Chen, Yuda Xiong

Object detection has long been dominated by traditional coordinate regression-based models, such as YOLO, DETR, and Grounding DINO. Although recent efforts have attempted to leverage MLLMs to tackle this task, they face challenges like low recall rate, duplicate predictions, coordinate misalignment, etc. In this work, we bridge this gap and propose Rex-Omni,

Roee Leder

The Ricci curvature equations are a central subject of study in geometry. However, in the smooth real case, their linear analysis is often confined to settings in which the background metric is Einstein. In this paper, we establish solvability and uniqueness conditions for the linearized problem on any compact Riemannian manifold with boundary. These conditi

Yingyan Li, Shuyao Shang, Weisong Liu, Bing Zhan

Scaling Vision-Language-Action (VLA) models on large-scale data offers a promising path to achieving a more generalized driving intelligence. However, VLA models are limited by a ``supervision deficit'': the vast model capacity is supervised by sparse, low-dimensional actions, leaving much of their representational power underutilized. To remedy this, we pro

Caner Korkmaz, Brighton Nuwagira, Barış Coşkunuzer, Tolga Birdal

We present CuMPerLay, a novel differentiable vectorization layer that enables the integration of Cubical Multiparameter Persistence (CMP) into deep learning pipelines. While CMP presents a natural and powerful way to topologically work with images, its use is hindered by the complexity of multifiltration structures as well as the vectorization of CMP. In fac

Howard Chen, Matthew S. Clement, Le-Chris Wang, Jesse T. Gu

It is still unclear whether exoplanets in compact multiplanet systems such as TRAPPIST-1 are able to accrete large quantities of volatiles, grow to sufficient mass, and maintain robust atmospheres and hydrospheres. Previous estimates of water content in M-dwarf systems have largely relied on population synthesis or atmosphere-interior evolution models, often

Long Cui, Weiyun Wang, Jie Shao, Zichen Wen

Existing Multimodal Large Language Models (MLLMs) suffer from increased inference costs due to the additional vision tokens introduced by image inputs. In this work, we propose Visual Consistency Learning (ViCO), a novel training algorithm that enables the model to represent images of varying semantic complexities using different numbers of vision tokens. Th

J. A. Vázquez-Mata, H. M. Hernández-Toledo, V. Avila-Reese, A. Rodríguez-Puebla

We present the MaNGA Visual Morphology (MVM) catalogue, featuring a visual morphological classification of 10,059 galaxies in the final MaNGA sample. By combining SDSS and DESI Legacy Survey (DLS) images, we classified galaxies into 13 Hubble types, detected tidal features, categorized bars into different families, and estimated concentration, asymmetry, and

Zamiul Alam, Christopher V. Cappiello, Francesc Ferrer

Inelastic dark matter (IDM) models feature an energy threshold for scattering with Standard Model particles, which enables their consistency with the increasingly stringent limits placed by direct detection experiments. In a typical construction, elastic scattering is absent at tree level, and a lighter dark matter state must first upscatter into a heavier s

Kevin Li, Manuel Brack, Sudeep Katakol, Hareesh Ravi

Although recent advances in visual generation have been remarkable, most existing architectures still depend on distinct encoders for images and text. This separation constrains diffusion models' ability to perform cross-modal reasoning and knowledge transfer. Prior attempts to bridge this gap often use the last layer information from VLM, employ multiple vi

C. Terry, J. Wolf

We show that a subset of $\mathbb{F}_{p}^{n}$ of $\mathrm{VC_{2}}$-dimension at most $k$ is well approximated by a union of atoms of a quadratic factor of complexity $(\ell,q)$ (denoting the complexities of the linear and quadratic part, respectively), where $\ell$ and $q$ are bounded by a constant depending only on $k$ and the desired level of approximation

Daniel Feijoo, Paula Garrido-Mellado, Marcos V. Conde, Jaesung Rim

This paper reviews the AIM 2025 Efficient Real-World Deblurring using Single Images Challenge, which aims to advance in efficient real-blur restoration. The challenge is based on a new test set based on the well known RSBlur dataset. Pairs of blur and degraded images in this dataset are captured using a double-camera system. Participant were tasked with deve

Felix Taubner, Ruihang Zhang, Mathieu Tuli, Sherwin Bahmani

Digital human avatars aim to simulate the dynamic appearance of humans in virtual environments, enabling immersive experiences across gaming, film, virtual reality, and more. However, the conventional process for creating and animating photorealistic human avatars is expensive and time-consuming, requiring large camera capture rigs and significant manual eff

Dantong Niu, Yuvan Sharma, Baifeng Shi, Rachel Ding

Robotic manipulation policies often struggle to generalize to novel objects, limiting their real-world utility. In contrast, cognitive science suggests that children develop generalizable dexterous manipulation skills by mastering a small set of simple toys and then applying that knowledge to more complex items. Inspired by this, we study if similar generali

Yoshi Nike Emilia Eschen, Thomas G. Wilson, Andrea Bonfanti, Carina M. Persson

A crucial chemical link between stars and their orbiting exoplanets is thought to exist. If universal, this connection could affect the formation and evolution of all planets. Therefore, this potential vital link needs testing by characterising exoplanets around chemically-diverse stars. We present the discovery of two planets orbiting the metal-poor, kinema

Congyao Zhang, Irina Zhuravleva, Annie Heinrich, Elena Bellomi

In this paper, we present extended gas kinematic maps of the Perseus cluster by combining five new XRISM/Resolve pointings observed in 2025 with four Performance Verification datasets from 2024, totaling 745 ks net exposure. To date, Perseus remains the only cluster that has been extensively mapped out to ~0.7$r_{2500}$ by XRISM/Resolve, while simultaneously

Yacouba Diarra, Nouhoum Souleymane Coulibaly, Michael Leventhal

Creating speech datasets for low-resource languages is a critical yet poorly understood challenge, particularly regarding the actual cost in human labor. This paper investigates the time and complexity required to produce high-quality annotated speech data for a subset of low-resource languages, low literacy Predominately Oral Languages, focusing on Bambara,

Colin Davalo, Parker Evans

Let $S$ be a closed surface of genus $g \geq 2$. We study the cocompact domain of discontinuity $\Omega_{\rho}$ in the Einstein universe $\mathrm{Ein}^{p-1,p}$ defined by Guichard-Wienhard and Kapovich-Leeb-Porti for a class of $p$-Anosov representations $\rho:\pi_1S \rightarrow \mathrm{SO}_0(p,p+1)$ including Hitchin representations, for $p \geq 3$. The quo

Ugur Akpinar, Erdem Sahin, Tina M. Hayward, Apratim Majumder

We present a new computational near-eye display method that addresses the vergence-accommodation conflict problem in stereoscopic displays through accommodation-invariance. Our system integrates a refractive lens eyepiece with a novel wavefront coding diffractive optical element, operating in tandem with a pre-processing convolutional neural network. We empl

Stefan Andreas Baumann, Nick Stracke, Timy Phan, Björn Ommer

Understanding the dynamics of a physical scene involves reasoning about the diverse ways it can potentially change, especially as a result of local interactions. We present the Flow Poke Transformer (FPT), a novel framework for directly predicting the distribution of local motion, conditioned on sparse interactions termed "pokes". Unlike traditional methods

Brian Street

We introduce Besov and Triebel--Lizorkin spaces on a manifold with boundary adapted to H\"ormander vector fields, near a so-called non-characteristic point of the boundary. We prove sharp results in these spaces for the corresponding restriction and trace operators, show these operators are retractions, and other related results. This is the second paper in

Yu-Zhen Janice Chen, Laurent Massoulié, Don Towsley

We investigate whether Gaussian Boson Sampling (GBS) can provide a computational advantage for solving the planted biclique problem, which is a graph problem widely believed to be classically hard when the planted structure is small. Although GBS has been heuristically and experimentally observed to favor sampling dense subgraphs, its theoretical performance

Sergio Serrano de Haro Iváñez, Joshua W. Moore, Lucile Grzesiak, Eoghan J. Mullholand

Mammalian tissue architecture is central to biological function, and its disruption is a hallmark of disease. Medical imaging techniques can generate large point cloud datasets that capture changes in the cellular composition of such tissues with disease progression. However, regions of interest (ROIs) are usually defined by quadrat-based methods that ignore

Barbara Šoda, Pierre-Antoine Graham, T. Rick Perche, Gurpahul Singh

We introduce a novel method that simultaneously isolates a quantum computer from decoherence and enables the controlled implementation of computational gates. We demonstrate a quantum computing model that utilizes a qubit's motion to protect it from decoherence. We model a qubit interacting with a quantum field via the standard light-matter interaction model

Isaac Gibbs, Ryan J. Tibshirani

We consider the problem of constructing probabilistic predictions that lead to accurate decisions when employed by downstream users to inform actions. For a single decision maker, designing an optimal predictor is equivalent to minimizing a proper loss function corresponding to the negative utility of that individual. For multiple decision makers, our proble

Fengzhi Guo, Chih-Chuan Hsu, Sihao Ding, Cheng Zhang

Reconstructing dynamic 3D scenes from monocular input is fundamentally under-constrained, with ambiguities arising from occlusion and extreme novel views. While dynamic Gaussian Splatting offers an efficient representation, vanilla models optimize all Gaussian primitives uniformly, ignoring whether they are well or poorly observed. This limitation leads to m

C. Terry, J. Wolf

In prior work, we showed that subsets of $\mathbb{F}_{p}^{n}$ of $\mathrm{VC_{2}}$-dimension at most $k$ are well approximated by a union of atoms of a quadratic factor of complexity $(\ell,q)$, where the complexity $\ell$ of the linear part and the complexity $q$ of the quadratic part are both bounded in terms of $k$, $p$, and the desired level of approxima

Łukasz Borchmann

Linguistic commentary on LLMs, heavily influenced by the theoretical frameworks of de Saussure and Chomsky, is often speculative and unproductive. Critics challenge whether LLMs can legitimately model language, citing the need for "deep structure" or "grounding" to achieve an idealized linguistic "competence." We argue for a radical shift in perspective towa

Bruno Longarela, Marcos V. Conde, Alvaro Garcia, Radu Timofte

This paper presents a comprehensive study and benchmark on Efficient Perceptual Super-Resolution (EPSR). While significant progress has been made in efficient PSNR-oriented super resolution, approaches focusing on perceptual quality metrics remain relatively inefficient. Motivated by this gap, we aim to replicate or improve the perceptual results of Real-ESR

Thomas Wimmer, Prune Truong, Marie-Julie Rakotosaona, Michael Oechsle

We introduce AnyUp, a method for feature upsampling that can be applied to any vision feature at any resolution, without encoder-specific training. Existing learning-based upsamplers for features like DINO or CLIP need to be re-trained for every feature extractor and thus do not generalize to different feature types at inference time. In this work, we propos

Rui Gonçalves, Vitor Miguel Ribeiro, Roman Chertovskih, António Pedro Aguiar

This study presents the implementation of a short-term forecasting system for price movements in exchange markets, using market depth data and a systematic procedure to enable a fully automated trading system. The case study focuses on the UK to Win Horse Racing market during the pre-live stage on the world's leading betting exchange, Betfair. Innovative con

Saurabh Sihag, Gonzalo Mateos, Alejandro Ribeiro

Neurodegeneration, characterized by the progressive loss of neuronal structure or function, is commonly assessed in clinical practice through reductions in cortical thickness or brain volume, as visualized by structural MRI. While informative, these conventional approaches lack the statistical sophistication required to fully capture the spatially correlated

Ran Azouri, Emil Jacobsen

We prove a motivic enhancement of the classical Picard--Lefschetz formula. Our proof is completely motivic, and yields a description of the motivic nearby cycles at a quasi-homogeneous singularity, as well as its monodromy, in terms of an embedding of projective hypersurfaces.

Yu Meng, Debashis Saha, Mikkel Thorbjørn Mikkelsen, Clara Henke

Photons are central to quantum technologies, with photonic qubits offering a promising platform for quantum communication. Semiconductor quantum dots stand out for their ability to generate single photons on demand, a key capability for enabling long-distance quantum networks. In this work, we utilize high-purity single-photon sources based on self-assembled

Ravindra Singh, Kiran Meena, Kapish Chand Meena

This paper presents general forms of Casorati inequalities for Riemannian maps and Riemannian submersions between Riemannian manifolds. Using these general forms, we obtain Casorati inequalities for Riemannian maps (resp. submersions) whose target (resp. source) spaces are generalized complex and generalized Sasakian space forms. As a consequence, we give Ca

Tomasz Cieślak, Jacek Jendrej, Christian Stinner

In the present manuscript, we calculate the exponential rate of convergence of the heated string system (a mixed-type hyperbolic-parabolic system of PDEs) towards the equilibrium, independently of the initial data. As a by-product of our analysis, we obtain an enhanced time decay of the solution. The main tool of our reasoning consists of asymptotic analysis