July 2025 arXiv papers — page 2

Seokchang Hong

We establish global existence and derive sharp pointwise decay estimates of solutions to cubic Dirac and Dirac-Klein-Gordon systems on a curved background, close to the Minkowski spacetime. By squaring the Dirac operator, we reduce the analysis to a nonlinear wave-type equation involving spinorial connections, and apply energy estimates based on vector field

Xiao Zhang, Johan bos

Neural semantic parsers have shown good overall performance for a variety of linguistic phenomena, reaching semantic matching scores of more than 90%. But how do such parsers perform on strongly context-sensitive phenomena, where large pieces of semantic information need to be duplicated to form a meaningful semantic representation? A case in point is Englis

Md. Ehsanul Haque, Abrar Fahim, Shamik Dey, Syoda Anamika Jahan

Early and accurate detection of the bone fracture is paramount to initiating treatment as early as possible and avoiding any delay in patient treatment and outcomes. Interpretation of X-ray image is a time consuming and error prone task, especially when resources for such interpretation are limited by lack of radiology expertise. Additionally, deep learning

Maimouna Baldé, Abdoul O. Diakité, Claudia Moreau, Gleb Bezgin

Multimodal high-dimensional data are increasingly prevalent in biomedical research, yet they are often compromised by block-wise missingness and measurement errors, posing significant challenges for statistical inference and prediction. We propose AdapDISCOM, a novel adaptive direct sparse regression method that simultaneously addresses these two pervasive i

Zhiyu Yin, James F. Drake, Marc Swisdak

The mechanisms controlling the relative heating and energization of electrons and protons during magnetic reconnection are explored. Simulations are carried out with the kglobal model, which produces bulk heating and the extended powerlaw distributions of both species that have been documented in observations. The simulations have been carried out with a ran

Thomas Ayral

Near-term quantum processors are limited in terms of the number of qubits and gates they can afford. They nevertheless give unprecedented access to programmable quantum systems that can efficiently, although imperfectly, simulate quantum time evolutions. Dynamical mean field theory, on the other hand, maps strongly-correlated lattice models like the Hubbard

Md. Ehsanul Haque, S. M. Jahidul Islam, Shakil Mia, Rumana Sharmin

Liver diseases are a serious health concern in the world, which requires precise and timely diagnosis to enhance the survival chances of patients. The current literature implemented numerous machine learning and deep learning models to classify liver diseases, but most of them had some issues like high misclassification error, poor interpretability, prohibit

Wil M. P. van der Aalst

The uptake of Artificial Intelligence (AI) impacts the way we work, interact, do business, and conduct research. However, organizations struggle to apply AI successfully in industrial settings where the focus is on end-to-end operational processes. Here, we consider generative, predictive, and prescriptive AI and elaborate on the challenges of diagnosing and

Amichay Vardi, Doron Cohen

We consider a minimal model for quantum thermalization of coupled chaotic subsystems. The route towards ergodicity is explored as a function of the coupling strength. The results are contrasted with the predictions of standard Random Matrix Theory (RMT) and the Eigenstates Thermalization Hypothesis (ETH). We highlight a coupling regime of disparity between t

Elias Hess-Childs, Keefer Rowan

For all $\alpha \in (0,1)$, we construct an explicit divergence-free vector field $V \in L^\infty([0,1],C^\alpha(\mathbb{T}^2))$ that exhibits universal anomalous (total) dissipation, accelerating dissipation enhancement, Richardson dispersion, anomalous regularization, and spatial intermittency. Additionally, we demonstrate the sharpness of the intermittent

J. A. S. Lima, M. H. Benetti

An extension of Maxwell's original prescription for an ideal gas is adopted to derive a broad class of Kappa-type velocity distributions, encompassing both fat and short-tailed forms. Within this general framework, a physically consistent fat-tailed Kappa distribution is identified that accurately fits recent suprathermal data. In particular, a kinetic physi

Chengyao Zhang, Ankang Gao, Xiaojue Zhu

Recent theoretical and experimental investigations have revealed that flapping compliant membrane wings can significantly enhance propulsive performance (e.g. Tzezana and Breuer, 2019, J. Fluid Mech., 862, 871-888) and energy harvesting efficiency (e.g. Mathai et al., 2022, J. Fluid Mech., 942, R4) compared to rigid foils. Here, we numerically investigate th

Léo Pioge, Kamil K. Pietrasz, Benoit Seron, Leonardo Novo

We prove a logical implication between two old conjectures stated by Bapat and Sunder about the permanent of positive semidefinite matrices. Although Drury has recently disproved both conjectures, this logical implication yields a non-trivial link between two seemingly unrelated conditions that a positive semidefinite matrix may fulfill. As a corollary, the

Mingda Chen, Yang Li, Xilun Chen, Adina Williams

Long-form factuality evaluation assesses the ability of models to generate accurate, comprehensive responses to short prompts. Existing benchmarks often lack human verification, leading to potential quality issues. To address this limitation, we introduce FACTORY, a large-scale, human-verified prompt set. Developed using a model-in-the-loop approach and refi

J. M. Greben

In the standard FRW formalism, the scale factor is assumed to describe the expansion of the universe. However, by examining empty space with a positive cosmological constant (i.e., a de Sitter space), we find that this assumption is incorrect. When described in conformal time, the associated conformal metric exhibits a big bang singularity where the effectiv

Jan Simson

Interactive data visualization is a major part of modern exploratory data analysis, with web-based technologies enabling a rich ecosystem of both specialized and general tools. However, current visualization tools often lack support for transformation or wrangling of data and are forced to re-implement their own solutions to load and ingest data. This redund

Ernest Bonnah, Luan Viet Nguyen, Khaza Anuarul Hoque

Hyperproperties for Time Window Temporal Logic (HyperTWTL) is a domain-specific formal specification language known for its effectiveness in compactly representing security, opacity, and concurrency properties for robotics applications. This paper focuses on HyperTWTL-constrained secure reinforcement learning (SecRL). Although temporal logic-constrained safe

Ivan Ahumada, Patrick Copinger, James P. Edwards, Karthik Rajeev

The modeling of present and future ultra-intense lasers demands techniques that go beyond the standard diagrammatic approach to non-perturbatively fully capture the effects of strong fields. We illustrate the first-quantized path integral representation for strong-field quantum electrodynamics as a means of accessing the laser being treated as a background f

Hong Jiang, Jinnawat Jongkhumkrong, Y. J. Chao, Qian Wang

While aquaporin (AQP) gating dynamically regulates transmembrane water permeability for cellular homeostasis, its mechanisms remain poorly understood compared to ion channels. A central challenge is the lack of methods to measure water flow through AQPs with the spatiotemporal resolution and sensitivity equivalent to patch-clamp recordings of ion fluxes, a l

Guilherme Guerino, Luiz Rodrigues, Luana Bianchini, Mariana Alves

This study explores the integration of Augmented Intelligence (AuI) in Intelligent Tutoring Systems (ITS) to address challenges in Artificial Intelligence in Education (AIED), including teacher involvement, AI reliability, and resource accessibility. We present MathAIde, an ITS that uses computer vision and AI to correct mathematics exercises from student wo

Priyanka Saini, Praveen K. Yadav, M. S. Mehta, M. Bhuyan

We employ the relativistic mean-field (RMF) approach with NL3 parameters to study shell and sub-shell closures in the isotopic chains of Cl, Ar, K, Ca, Sc, Ti, V, and Cr nuclei. By analyzing nuclear bulk properties, binding energy, charge radii, two-neutron separation energies, deformation parameters ($\beta_2$), and single-particle levels we trace the evolu

Danny Scott, William LaForest, Hritom Das, Ioannis Polykretis

The deployment of dense, low-cost sensors is critical for realizing ubiquitous smart environments. However, existing sensing solutions struggle with the energy, scalability, and reliability trade-offs imposed by battery maintenance, wireless transmission overhead, and data processing complexity. In this work, we present Vibe2Spike, a novel battery-free, wire

Alena Kopaničáková, Youngkyu Lee, George Em Karniadakis

We propose a new deflation strategy to accelerate the convergence of the preconditioned conjugate gradient(PCG) method for solving parametric large-scale linear systems of equations. Unlike traditional deflation techniques that rely on eigenvector approximations or recycled Krylov subspaces, we generate the deflation subspaces using operator learning, specif

Shiyao Sang, Yinggang Ling

Robotic middleware is fundamental to ensuring reliable communication among system components and is crucial for intelligent robotics, autonomous vehicles, and smart manufacturing. However, existing robotic middleware often struggles to meet the diverse communication demands, optimize data transmission efficiency, and maintain scheduling determinism between O

Paul Apisa, Matt Bainbridge, Jane Wang

We identify the moduli space of complex affine surfaces with the moduli space of regular meromorphic connections on Riemann surfaces and show that it satisfies a corresponding universal property. As a consequence, we identify the tangent space of the moduli space of affine surfaces, at an affine surface X, with the first hypercohomology of a two-term sequenc

Liza Arzhakova, Gabriel Calsamiglia, Bertrand Deroin

In this paper we prove the connectedness of isoperiodic moduli spaces of meromorphic differentials with at least three simple poles on homologically marked smooth curves whose periods are either not contained in a real line, or not contained in the rational space generated by the peripheral periods. From this topological property we deduce dynamical properti

Ashkan Shakarami, Yousef Yeganeh, Azade Farshad, Lorenzo Nicole

This paper introduces Stress-Aware Learning, a resilient neural training paradigm in which deep neural networks dynamically adjust their optimization behavior - whether under stable training regimes or in settings with uncertain dynamics - based on the concept of Temporary (Elastic) and Permanent (Plastic) Deformation, inspired by structural fatigue in mater

Zhigen Zhao, Liuchuan Yu, Ke Jing, Ning Yang

The rapid advancement of Vision-Language-Action models has created an urgent need for large-scale, high-quality robot demonstration datasets. Although teleoperation is the predominant method for data collection, current approaches suffer from limited scalability, complex setup procedures, and suboptimal data quality. This paper presents XRoboToolkit, a cross

Zachary K. Stine, James E. Deitrick

Greater theorizing of methods in the computational humanities is needed for epistemological and interpretive clarity, and therefore the maturation of the field. In this paper, we frame such modeling work as engaging in translation work from a cultural, linguistic domain into a computational, mathematical domain, and back again. Translators benefit from artic

Lorenzo Formaggio, Francesco Di Clemente, Geetika Yadav, Alessandro Drago

We compute the full cosmic trajectories of the early Universe across the QCD phase diagram as the plasma cools from $T\simeq500\,$MeV to $30\,$MeV, assuming $\beta$-equilibrated matter. The trajectories are obtained by simultaneously solving baryon-number, electric-charge, and lepton-asymmetry conservation, closed by a state-of-the-art lattice-QCD equation o

Shuang Zeng, Chee Hong Lee, Kaiwen Li, Boxu Xie

Retinal vessel segmentation plays a vital role in analyzing fundus images for the diagnosis of systemic and ocular diseases. Building on this, classifying segmented vessels into arteries and veins (A/V) further enables the extraction of clinically relevant features such as vessel width, diameter and tortuosity, which are essential for detecting conditions li

Lucas Alves Zischler, Chiara Lasagni, Paolo Serena, Alberto Bononi

Wideband systems experience significant inter-channel stimulated Raman scattering (ISRS) and channel-dependent losses. Due to the non-uniform attenuation profile, the combined effects of ISRS and fiber loss can only be accurately estimated using numerical methods. In this work, we present an approximate closed-form expression for the channels' power profile

Andreas Swerdlow

We extend the multivariate Fa\`{a} di Bruno formula to the super case, where anticommuting odd coordinates are considered. The formula takes the same form as the classical case but contains some nontrivial signs, which essentially measure the failure to order the odd factors and derivatives optimally. As a quick application, we obtain an explicit combinatori

Chandler Smith, HanQin Cai, Abiy Tasissa

The problem of recovering the configuration of points from their partial pairwise distances, referred to as the Euclidean Distance Matrix Completion (EDMC) problem, arises in a broad range of applications, including sensor network localization, molecular conformation, and manifold learning. In this paper, we propose a Riemannian optimization framework for so

Ian Herzog

While much work has gone into associating neutrino emission with various sources, very few sources have emerged. With the recent publication of IceCube Event Catalog (IceCat-1), the IceCube neutrino observatory has released a list of the most promising astrophysical neutrino events since operations began in 2010. Using the archival data from the High Altitud

Kangrui Liu, Lingxiao Wang, Yan Li

Nonprobability samples have rapidly emerged to address time-sensitive priority topics in a variety of fields. While these data are timely, they are prone to selection bias. To mitigate selection bias, a large number of survey research literature has explored the use of propensity score (PS) adjustment methods to enhance population representativeness of nonpr

Mateo de Mayo, Daniel Cremers, Taihú Pire

Humanoid robots and mixed reality headsets benefit from the use of head-mounted sensors for tracking. While advancements in visual-inertial odometry (VIO) and simultaneous localization and mapping (SLAM) have produced new and high-quality state-of-the-art tracking systems, we show that these are still unable to gracefully handle many of the challenging setti

Samuel D. Upton, Barry Wardell, Adam Pound, Niels Warburton

Recent years have seen the first production of "post-adiabatic" gravitational-waveform models based on second-order gravitational self-force theory. These models rely on calculations of an effective source in the perturbative second-order Einstein equation. Here, for the first time, we detail the calculation of the effective source in a Schwarzschild backgro

Ashutosh Bandooni, Brindha Subburaj

Benchmarks for evaluating reasoning among Vision Language Models (VLMs) on several fields and domains are being curated more frequently over the last few years. However these are often monolingual, mostly available in English. Additionally there also is a lack of datasets available in Hindi on tasks apart from comprehension and translation. We introduce Gani

Kelly Kendro, Jeffrey Maloney, Scott Jarvis

The degree to which large language models (LLMs) produce writing that is truly human-like remains unclear despite the extensive empirical attention that this question has received. The present study addresses this question from the perspective of lexical diversity. Specifically, the study investigates patterns of lexical diversity in LLM-generated texts from

Raiyaan Abdullah, Jared Claypoole, Michael Cogswell, Ajay Divakaran

Action recognition models demonstrate strong generalization, but can they effectively transfer high-level motion concepts across diverse contexts, even within similar distributions? For example, can a model recognize the broad action "punching" when presented with an unseen variation such as "punching person"? To explore this, we introduce a motion transfera

Diego Lobos Maturana

The category $\bcalNT$ is a category of certain commutative graded algebras over a field. It was introduced in \cite{Lobos2} as a generalization of algebras generated by Jucys-Murphy elements in the many \textbf{End} algebras of the diagrammatic Soergel category of Elias and Williamson. In the first part of this article we define certain \emph{Invariants} fo

Yihong Dong, Xue Jiang, Jiaru Qian, Tian Wang

Code generation agents powered by large language models (LLMs) are revolutionizing the software development paradigm. Distinct from previous code generation techniques, code generation agents are characterized by three core features. 1) Autonomy: the ability to independently manage the entire workflow, from task decomposition to coding and debugging. 2) Expa

Aditya Kumar, Sadeed Hameed, Thibaud Denneulin, Aravind Puthirath Balan

Van der Waals (vdW) magnetic heterostructures offer a versatile platform for engineering interfacial spin interactions with atomic precision, enabling nontrivial spin textures and dynamic behaviors. In this work, we report robust asymmetric magnetization reversal and exchange bias in Fe3GeTe2 (FGT), driven by interlayer exchange coupling with the A-type anti

Fred Mutisya, Shikoh Gitau, Nasubo Ongoma, Keith Mbae

HealthBench, a benchmark designed to measure the capabilities of AI systems for health better (Arora et al., 2025), has advanced medical language model evaluation through physician-crafted dialogues and transparent rubrics. However, its reliance on expert opinion, rather than high-tier clinical evidence, risks codifying regional biases and individual clinici

Alexander Farutin

The dynamics of a rigid particle above a fluid-fluid interface in shear flow is studied here numerically and analytically as a function of the downward force applied on the particle. It is found here that the particle goes below the equilibrium level of the interface for a strong enough downward force. Such states remain stable under flow, with a fluid film

Oshayer Siddique, J. M Areeb Uzair Alam, Md Jobayer Rahman Rafy, Syed Rifat Raiyan

The discipline of physics stands as a cornerstone of human intellect, driving the evolution of technology and deepening our understanding of the fundamental principles of the cosmos. Contemporary literature includes some works centered on the task of solving physics problems - a crucial domain of natural language reasoning. In this paper, we evaluate the per

Imen Mahmoud, Andrei Velichko

This study proposes a novel methodological framework integrating a LightGBM regression model and genetic algorithm (GA) optimization to systematically evaluate the contribution of COVID-19-related indicators to Bitcoin return prediction. The primary objective was not merely to forecast Bitcoin returns but rather to determine whether including pandemic-relate

Soumya Kanti Pal, Lea F Santos

Understanding the stability of integrability in many-body quantum systems is key to controlling dynamics and predicting thermalization. While the breakdown of integrability in short-range interacting systems is well understood, the role of long-range couplings -- ubiquitous and experimentally realizable -- remains unclear. We show that in fully connected mod

Sarah G. A. Barbosa, Raissa Estrela, Paulo C. F. da Silva Filho, Daniel B. de Freitas

Upcoming direct-imaging missions like the Habitable Worlds Observatory (HWO) aim to characterize dozens of Earth-like exoplanets by capturing their reflected-light spectra. However, traditional atmospheric retrieval frameworks are too computationally intensive to explore the high-dimensional parameter spaces such missions will generate. Here, we present a on

R. F. L. Holanda, M. Ferreira, Javier E. Gonzalez

In this paper, we analyze the potential variation of the gravitational constant $G$ using data from strong gravitational lensing systems and Type Ia supernovae. Testing $G(z)$ parameterizations where $G(z) = G_0(1 + G_1z)$ and $G(z) = G_0(1 + z)^{G_1}$, we also account for the influence of $G$ on the luminosity of SNe Ia through the Chandrasekhar mass-lumino

William Keith, Rishi Nath, James Sellers

We consider simultaneous $(s,s+t,s+2t,\dots,s+pt)$-core partitions in the large-$p$ limit, or (when $s<t$), partitions in which no hook may be of length $s \pmod{t}$. We study generating functions, containment properties, and congruences when $s$ is not coprime to $t$. As a boundary case of the general study made by Cho, Huh and Sohn, we provide enumerations

Yiqi Liu, Susanna Azzoni, Susan E. Clark, Brandon S. Hensley

We investigate how dust foreground complexity can affect measurements of the tensor-to-scalar ratio, $r$, in the context of the Simons Observatory, using a cross-spectrum component separation analysis. Employing a suite of simulations with realistic Galactic dust emission, we find that spatial variation in the dust frequency spectrum, parametrized by $\beta_

Marcus J Clark, Obada Alia, Sima Bahrani, Gregory T Jasion

We experimentally demonstrate the coexistence of three entanglement-based quantum channels with carrier-grade classical optical channels over $11.5$km hollow core nested antiresonant nodeless fibre, in a four user quantum network. A transmission of $800$Gbps is achieved with four classical channels simultaneously with three quantum channels all operating in

Yarden Eilat Bloch, Dovi Poznanski, Nick L. J. Cox, Emmanuel Bernhard

The Gaia mission is transforming our view of the Milky Way by providing distances towards a billion stars, and much more. The third data release includes nearly a million spectra from its Radial Velocity Spectrometer (RVS). Identifying unexpected features in such vast datasets presents a significant challenge. It is impossible to visually inspect all of the

Liwei Chen, Tong Qin, Zhenhua Huangfu, Li Li

We propose a differentiable optimization framework for flip-and-landing trajectory design of reusable spacecraft, exemplified by the Starship vehicle. A deep neural network surrogate, trained on high-fidelity CFD data, predicts aerodynamic forces and moments, and is tightly coupled with a differentiable rigid-body dynamics solver. This enables end-to-end gra

Deniz Bilman, Peter D. Miller

General rogue waves of infinite order constitute a family of solutions of the focusing nonlinear Schr\"odinger equation that have recently been identified in a variety of asymptotic limits such as high-order iteration of B\"acklund transformations and semiclassical focusing of pulses with specific amplitude profiles. These solutions have compelling propertie

M. Scalco, R. Gerasimov, L. R. Bedin, E. Vesperini

This paper presents the first study of the most massive globular cluster (GC) in the Milky Way, omega Centauri, employing recently acquired JWST deep images. By combining these data with archival Hubble Space Telescope (HST) images, we derived proper motions (PMs) for a significant portion of the JWST field. Our analysis of the colour-magnitude diagram (CMD)

M. Schuyler Moss, Alev Orfi, Christopher Roth, Anirvan M. Sengupta

Neural quantum states (NQS) provide flexible and compact wavefunction parameterizations for numerical studies of quantum many-body physics. In particular, NQS aim to circumvent the exponential scaling of the Hilbert space by compressing quantum many-body wavefunctions with a tractable amount of parameters. While inspired by deep learning, it remains unclear

Guilherme Limberg, Vinicius M. Placco, Alexander P. Ji, Yupeng Yao

We report on the discovery of GDR3_526285 ($Gaia$ DR3 Source ID 5262850721755411072), a star with $\rm[Fe/H] = -4.82 \pm 0.25$ and one of the lowest metal ($\text{atomic number} > 2$) mass fractions ever found ($Z_{\rm GDR3\_526585} \lesssim 1.0 \times 10^{-6}$). We first identified it as an ultra metal-poor (UMP; $\rm[Fe/H] < -4$) red giant-branch (RGB) sta

Shi-Fan Chen, Cullan Howlett, Yan Lai, Fei Qin

We present predictions for redshift-space peculiar velocity statistics in the Lagrangian and Eulerian formulations of the effective field theory (EFT) of large-scale structure. We compute 2-point pairwise velocity statistics up to the second moment at next-to-leading (1-loop) order, showing that they can be modeled together with redshift-space galaxy densiti

Christian Dahlhausen, Jeroen Hekking, Storm Wolters

We prove a duality statement on modules over KH-theory in the stable motivic homotopy category whose dualizing object is given by G-theory, over any quasi-excellent scheme of characteristic zero.

Barry M. Dillon, Jim Harkin, Aqib Javed

Anomaly detection offers a promising strategy for discovering new physics at the Large Hadron Collider (LHC). This paper investigates AutoEncoders built using neuromorphic Spiking Neural Networks (SNNs) for this purpose. One key application is at the trigger level, where anomaly detection tools could capture signals that would otherwise be discarded by conve

Lieke Sippens Groenewegen, Sanjana Curtis, Philipp Mösta, Daniel Kasen

We investigate the kilonova emission resulting from outflows produced in a three-dimensional (3D) general-relativistic magnetohydrodynamic (GRMHD) simulation of a hypermassive neutron star (HMNS) remnant. We map the outflows into the FLASH hydrodynamics code to model their expansion in axisymmetry, and study the effects of employing different $r$-process hea

Jatin Narde, Onkar Parrikar, Harshit Rajgadia, Sandip Trivedi

The Ryu-Takayanagi (RT) formula and its interpretation in terms of quantum error correction (QEC) implies an emergent locality for the spread of quantum information in holographic CFTs, where information injected at a point in the boundary theory spreads within a sharp light-cone corresponding to the butterfly velocity. This emergent locality is a necessary

Sarah L. Nickerson, Naseem Rangwala, Keeyoon Sung, Xinchuan Huang

We present the first astrophysical detection of methanol (CH3OH) in the torsional band near 25 um. Using high resolution mid-infrared (MIR) spectroscopy, we identified over seventy gas-phase CH3OH absorption lines between 20 and 28 um towards the massive protostar NGC 7538 IRS 1 with SOFIA/EXES. We derive a temperature of 180 K and a total column density of

Taige Wang, Ya-Hui Zhang

Intertwining intrinsic topological order with gapless collective modes remains a central challenge in many-body physics. We show that a quantum-Hall trilayer at $\nu_{1}=\nu_{2}=\nu_{3}= \frac13$, tuned solely by the inter-layer spacing $d$, realizes this goal. Large-scale density-matrix renormalization group (DMRG) calculations and a Chern-Simons field theo

Takumi S. Tanaka, Hollis B. Akins, Yuichi Harikane, John D. Silverman

JWST has revealed a new high-redshift population called little red dots (LRDs). Since LRDs may be in the early phase of black hole growth, identifying them in the early universe is crucial for understanding the formation of the first supermassive black holes. However, no robust LRD candidates have been identified at $z>10$, because commonly-used NIRCam photo

Samuel Leutheusser, Hong Liu

We propose a new way to understand the volume of certain subregions in the bulk of AdS spacetime by relating it to an algebraic quantity known as the index of inclusion. This index heuristically measures the relative size of a subalgebra $\mathcal{N}$ embedded within a larger algebra $\mathcal{M}$. According to subregion-subalgebra duality, bulk subregions a

Ewin Tang, John Wright

Many quantum algorithms, to compute some property of a unitary $U$, require access not just to $U$, but to $cU$, the unitary with a control qubit. We show that having access to $cU$ does not help for a large class of quantum problems. For a quantum circuit which uses $cU$ and $cU^\dagger$ and outputs $|\psi(U)\rangle$, we show how to "decontrol" the circuit

William DeRocco

Dark matter in the form of macroscopic composites is largely unconstrained at masses of $\sim 10^{11}- 10^{17}$ g. In this mass range, dark matter may collide with planetary bodies, depositing an immense amount of energy and leaving dramatic surface features that remain detectable on geological timescales. In this paper, we show that Ganymede, the largest Jo

Jie Zhu, Yiyang Su, Minchul Kim, Anil Jain

Whole-body biometric recognition is a challenging multimodal task that integrates various biometric modalities, including face, gait, and body. This integration is essential for overcoming the limitations of unimodal systems. Traditionally, whole-body recognition involves deploying different models to process multiple modalities, achieving the final outcome

Pierre-Gabriel Rozon, Kartiek Agarwal

We introduce a variational scheme inspired by classical shadow tomography to compute ground state correlations of quantum spin Hamiltonians. Shadow tomography allows for efficient reconstruction of expectation values of arbitrary observables from a bag of repeated, randomized measurements, called snapshots, on copies of the state $\rho$. The prescription all

Neil Dowling, Jacopo De Nardis, Markus Heinrich, Xhek Turkeshi

Unitary randomness underpins both fundamental tasks in quantum information and the modern theory of quantum chaos. On one side, a central concept is that of approximate unitary designs: circuits that look random according to small moments and for forward-in-time protocols. In a distinct setting, out-of-time-ordered correlators (OTOCs), intensely studied as a

Ewin Tang, John Wright

We prove that the generic quantum speedups for brute-force search and counting only hold when the process we apply them to can be efficiently inverted. The algorithms speeding up these problems, amplitude amplification and amplitude estimation, assume the ability to apply a state preparation unitary $U$ and its inverse $U^\dagger$; we give problem instances

Daisuke Fujii, Mitsuru Tanaka

The effect of the QCD scale anomaly on the internal pressure distribution of hadrons is studied based on the trace-traceless decomposition of the energy-momentum tensor. Using recent model-independent results of gravitational form factors as input, the pressure distributions of both pions and nucleons are analyzed in the instant form and the light-front form

Bowen Zhang, Sicheng Xu, Chuxin Wang, Jiaolong Yang

In this paper, we present a novel framework for video-to-4D generation that creates high-quality dynamic 3D content from single video inputs. Direct 4D diffusion modeling is extremely challenging due to costly data construction and the high-dimensional nature of jointly representing 3D shape, appearance, and motion. We address these challenges by introducing

Toka Alokda, Cristiano Porciani

The formation of dark-matter halos from small cosmological perturbations generated in the early universe is a highly non-linear process typically modeled through N-body simulations. In this work, we explore the use of deep learning to segment and classify proto-halo regions in the initial density field according to their final halo mass at redshift z=0. We c

Jessica Bader, Leander Girrbach, Stephan Alaniz, Zeynep Akata

Concept Bottleneck Models (CBMs) and other concept-based interpretable models show great promise for making AI applications more transparent, which is essential in fields like medicine. Despite their success, we demonstrate that CBMs struggle to reliably identify the correct concepts under distribution shifts. To assess the robustness of CBMs to concept vari

Archisman Panigrahi, Bitan Roy

Projected branes are constituted by only a small subset of sites of a higher-dimensional crystal, otherwise placed on a hyperplane oriented at an irrational or a rational slope therein, for which the effective Hamiltonian is constructed by systematically integrating out the sites of the parent lattice that fall outside such branes [Commun. Phys. 5, 230 (2022

Zihan Wang, Jeff Tan, Tarasha Khurana, Neehar Peri

We address the problem of dynamic scene reconstruction from sparse-view videos. Prior work often requires dense multi-view captures with hundreds of calibrated cameras (e.g. Panoptic Studio). Such multi-view setups are prohibitively expensive to build and cannot capture diverse scenes in-the-wild. In contrast, we aim to reconstruct dynamic human behaviors, s

Arash Arabi Ardehali, Christopher Beem, Madalena Lemos, Leonardo Rastelli

Vertex algebras that arise from four-dimensional, $\mathcal{N}=2$ superconformal field theories inherit a collection of novel structural properties from their four-dimensional ancestors. Crucially, when the parent SCFT is unitary, the corresponding vertex algebra is not unitary in the conventional sense. In this paper, we motivate and define a generalized no

Alexander C. Tyner, Vladimir Juricic, Bitan Roy

The interplay of disorder and dimensionality governs the emergence and stability of electronic phases in quantum materials and quantum phase transitions among them. While three-dimensional (3D) dirty Fermi liquids and Weyl semimetals support robust metallic states, undergoing disorder-driven Anderson localization transitions at strong disorder and the later

Miaosen Zhang, Ziqiang Xu, Jialiang Zhu, Qi Dai

With the development of multimodal reasoning models, Computer Use Agents (CUAs), akin to Jarvis from \textit{"Iron Man"}, are becoming a reality. GUI grounding is a core component for CUAs to execute actual actions, similar to mechanical control in robotics, and it directly leads to the success or failure of the system. It determines actions such as clicking

Li Siyao, Yao Feng, Omid Taheri, Chen Change Loy

While current general-purpose 3D human models (e.g., SMPL-X) efficiently represent accurate human shape and pose, they lacks the ability to physically interact with the environment due to the kinematic nature. As a result, kinematic-based interaction models often suffer from issues such as interpenetration and unrealistic object dynamics. To address this lim

Dian Chen, Yansong Qu, Xinyang Li, Ming Li

Current auto-regressive models can generate high-quality, topologically precise meshes; however, they necessitate thousands-or even tens of thousands-of next-token predictions during inference, resulting in substantial latency. We introduce XSpecMesh, a quality-preserving acceleration method for auto-regressive mesh generation models. XSpecMesh employs a lig

Yunxiang Yan, Tomohiro Sawada, Kartik Goyal

While question-answering~(QA) benchmark performance is an automatic and scalable method to compare LLMs, it is an indirect method of evaluating their underlying problem-solving capabilities. Therefore, we propose a holistic and generalizable framework based on \emph{cascaded question disclosure} that provides a more accurate estimate of the models' problem-s

Umesh Kumar, Rafal Rechcinski, Tatiana de Picoli, Jukka Vayrynen

Interfaces between topological insulators and superconductors are promising platforms for realizing Majorana zero modes (MZMs) via the superconducting proximity effect. We introduce a bilayer model consisting of the surface states of a three-dimensional topological insulator (3DTI) coupled to an $s$-wave superconductor and systematically study the role of in

Xihan Ji, Francesco D'Eugenio, Ignas Juodžbalis, Dominic J. Walton

Recent observations by the James Webb Space Telescope (JWST) have revealed a puzzling population of optically red and compact galaxies with peculiar "V"-shaped spectra at high redshift, known as "Little Red Dots" (LRDs). Until now, most spectroscopically confirmed LRDs are found at $z>4$ and it has been speculated that LRDs are tracing the early stages of bl

Di Li, Jie Feng, Jiahao Chen, Weisheng Dong

3D affordance reasoning, the task of associating human instructions with the functional regions of 3D objects, is a critical capability for embodied agents. Current methods based on 3D Gaussian Splatting (3DGS) are fundamentally limited to single-object, single-step interactions, a paradigm that falls short of addressing the long-horizon, multi-object tasks

Justin Kay, Grant Van Horn, Subhransu Maji, Daniel Sheldon

The widespread availability of off-the-shelf machine learning models poses a challenge: which model, of the many available candidates, should be chosen for a given data analysis task? This question of model selection is traditionally answered by collecting and annotating a validation dataset -- a costly and time-intensive process. We propose a method for act

Gerald Curran, Luke J. Weaver, Zachary Rex, Ivan Biaggio

Decoherence effects for entangled triplet pairs in organic molecular crystals are analyzed for the case when excitons can hop between inequivalent lattice sites. The fluorescence quantum beats caused by quantum interference upon triplet-triplet recombination into an emissive singlet state are predicted as a function of hopping time and magnetic field based o

Cecilia Fruet, Arthur Alexandre, Alia Abbara, Claude Loverdo

Complex spatial structure, with partially isolated subpopulations, and environment heterogeneity, such as gradients in nutrients, oxygen, and drugs, both shape the evolution of natural populations. We investigate the impact of environment heterogeneity on mutant fixation in spatially structured populations with demes on the nodes of a graph. When migrations

Nathan Wycoff, Ali Arab, Lisa O. Singh

In analyses with severe data-limitations, augmenting the target dataset with information from ancillary datasets in the application domain, called source datasets, can lead to significantly improved statistical procedures. However, existing methods for this transfer learning struggle to deal with situations where the source datasets are also limited and not

Jonathan R. Landers

This work advances distribution recovery from sparse data and ensemble classification through three main contributions. First, we introduce a closed-form estimator that reconstructs scaled beta distributions from limited statistics (minimum, maximum, mean, and median) via composite quantile and moment matching. The recovered parameters $(\alpha,\beta)$, when

Sahana Vasudevan

We prove a type of systolic inequality for embeddings of $T^2$ in $\mathbb{R}^3$. In particular, a highly twisted $T^2$ embedded in $\mathbb{R}^3$ must contain a non-contractible loop of small $\mathbb{R}^3$-diameter.

Dian Jing, Pablo Sala, Liang Jiang, Ruben Verresen

Topological order (TO) provides a natural platform for storing and manipulating quantum information. However, its stability to noise has only been systematically understood for Abelian TOs. In this work, we exploit the non-deterministic fusion of non-Abelian anyons to inform active error correction and design decoders where the fusion products, instead of fl

Chiung Hwang, Chang Lei, Yuezhang Tang

As an exact count of protected states, the superconformal index provides a powerful probe into holography and quantum aspects of gravity, reproducing the Bekenstein--Hawking entropy of supersymmetric AdS black holes in the large-$N$ limit. As a step toward understanding quantum black hole microstates, we study the finite-$N$ index of the 3d ADHM quiver gauge

Liu Li, Qiang Ma, Cheng Ouyang, Johannes C. Paetzold

Deep learning-based medical image segmentation techniques have shown promising results when evaluated based on conventional metrics such as the Dice score or Intersection-over-Union. However, these fully automatic methods often fail to meet clinically acceptable accuracy, especially when topological constraints should be observed, e.g., continuous boundaries

Xudong Sun, René Corbet, Carsten Marr

Multiparameter persistence module can capture more topological differences across data instances compared to using a single parameter, where the well-studied matching distance investigates the distance along a straight line in the multiparameter space that gives the biggest difference. We propose to generalize the straight line to a monotone path filtration