February 2025 arXiv papers — page 39

Shital Lawande, Kuldeep Saha

We study smooth proper embeddings of compact orientable surfaces in compact orientable $4$-manifolds and elements in the mapping class group of that surface which are induced by diffeomorphisms of the ambient $4$-manifolds. We call such mapping classes extendible. An embedding for which all mapping classes are extendible is called flexible. We show that for

Horst K. Hahn, Matthias S. May, Volker Dicken, Michael Walz

Lung cancer is the second most common cancer and the leading cause of cancer-related deaths worldwide. Survival largely depends on tumor stage at diagnosis, and early detection with low-dose CT can significantly reduce mortality in high-risk patients. AI can improve the detection, measurement, and characterization of pulmonary nodules while reducing assessme

Manuj Kant, Sareh Nabi, Manav Kant, Roland Scharrer

Legal services rely heavily on text processing. While large language models (LLMs) show promise, their application in legal contexts demands higher accuracy, repeatability, and transparency. Logic programs, by encoding legal concepts as structured rules and facts, offer reliable automation, but require sophisticated text extraction. We propose a neuro-symbol

Lizzie Buchanan, Huizheng Guo, Gabriel Montoya-Vega, Yongwu Rong

The concept of adequate links, introduced by Lickorish and Thistlethwaite as a generalization of alternating links, has recently gained interest among knot theorists in the context of Khovanov homology. Przytycki and Silvero introduced the more general concept of Khovanov adequacy: a diagram is Khovanov-adequate if its associated Khovanov chain complexes at

Marius Marinescu, Costel Balcau

In this paper we use a well know method in statistics, the $\delta$-method, to provide an asymptotic distribution for the Mutual Information, and construct and independence test based on it. Interesting connections are found with the likelihood ratio test and the chi-square goodness of fit test. In general, the difference between the Mutual Information evalu

Chengyu Wang, P. T. Madathil, S. K. Singh, A. Gupta

In the extreme quantum limit, when the Landau level filling factor $\nu<1$, the dominant electron-electron interaction in low-disorder two-dimensional electron systems leads to exotic many-body phases. The ground states at even-denominator $\nu=$ 1/2 and 1/4 are typically Fermi seas of composite fermions carrying two and four flux quanta, surrounded by the J

PIN AI Team, Bill Sun, Gavin Guo, Regan Peng

Personal AI assistants (e.g., Apple Intelligence, Meta AI) offer proactive recommendations that simplify everyday tasks, but their reliance on sensitive user data raises concerns about privacy and trust. To address these challenges, we introduce the Guardian of Data (GOD), a secure, privacy-preserving framework for training and evaluating AI assistants direc

Thomas R. Richardson, Immo C. Reis

The role of radiative corrections in low energy nuclear physics is beginning to receive more scrutiny. We examine the impact of these corrections for the deuteron charge form factor and the radiative capture process $np \to d \gamma$ through the velocity renormalization group. In both cases, we find percent level shifts in the relevant observables after evol

Önder Gürcan, Timo Szczepanska, Vanja Falck, Patrycja Antosz

Large-scale social digital twinning projects are complex with multiple objectives. For example, a social digital twinning platform for innovative last-mile delivery solutions may aim to assess consumer delivery method choices within their social environment. However, no single tool can achieve all objectives. Different simulators exist for consumer behavior

Yiting Liu, Hai Zhou, Jia Wang, Fan Yang

Placement is a critical task with high computation complexity in VLSI physical design. Modern analytical placers formulate the placement objective as a nonlinear optimization task, which suffers a long iteration time. To accelerate and enhance the placement process, recent studies have turned to deep learning-based approaches, particularly leveraging graph c

András A. Benczúr, Tibor Gyimóthy, Balázs Szegedy

Artificial intelligence (AI) has undergone remarkable development since the mid-2000s, particularly in the fields of machine learning and deep learning, driven by the explosive growth of large databases and computational capacity. Hungarian researchers recognized the significance of AI early on, actively participating in international research and achieving

Nikolaos Kidonakis

I present higher-order results for $s$-channel single-top-quark production that are derived from soft-gluon resummation. I show that the soft-gluon corrections provide the dominant contribution to the QCD corrections, and that they approximate very well the exact results through NNLO. Furthermore, approximate N$^3$LO corrections are computed by using the exp

Adam Marsh

General relativity and its extensions including torsion identify stress energy momentum as being proportional to the Einstein tensor, thus ensuring both symmetry and conservation. Here we visualize stress energy and momentum by identifying the associated relative fractional accelerations of geodesics encoded in the Einstein tensor. This also provides an intu

Sepehr Assadi, Helia Yazdanyar

The palette sparsification theorem (PST) of Assadi, Chen, and Khanna (SODA 2019) states that in every graph $G$ with maximum degree $\Delta$, sampling a list of $O(\log{n})$ colors from $\{1,\ldots,\Delta+1\}$ for every vertex independently and uniformly, with high probability, allows for finding a $(\Delta+1)$ vertex coloring of $G$ by coloring each vertex

Garima Chib, Parisa Mollaei, Amir Barati Farimani

G-Protein Coupled Receptors (GPCRs) are integral to numerous physiological processes and are the target of approximately one-third of FDA-approved therapeutics. Despite their significance, only a limited subset of GPCRs has been successfully targeted, primarily due to challenges in accurately modeling their structures. AlphaFold, a state-of-the-art deep lear

Jayadev Athreya, Pascal Hubert, Serge Troubetzkoy

We compute the complexity of the billiard language of the regular Euclidean $N$-gons (and other families of rational lattice polygons), answering a question posed by Cassaigne-Hubert-Troubetzkoy. Our key technical result is a counting result for saddle connections on lattice surfaces, when we count by combinatorial length.

Shinji Ito, Haipeng Luo, Taira Tsuchiya, Yue Wu

No-regret self-play learning dynamics have become one of the premier ways to solve large-scale games in practice. Accelerating their convergence via improving the regret of the players over the naive $O(\sqrt{T})$ bound after $T$ rounds has been extensively studied in recent years, but almost all studies assume access to exact gradient feedback. We address t

Md. Zahin Hossain George, Naimul Hossain, Md. Rafiuzzaman Bhuiyan, Abu Kaisar Mohammad Masum

There have recently been many cases of unverified or misleading information circulating quickly over bogus web networks and news portals. This false news creates big damage to society and misleads people. For Example, in 2019, there was a rumor that the Padma Bridge of Bangladesh needed 100,000 human heads for sacrifice. This rumor turns into a deadly positi

Christopher Zerafa, Pauline Galea, Cristiana Sebu

Full-Waveform Inversion seeks to achieve a high-resolution model of the subsurface through the application of multi-variate optimization to the seismic inverse problem. Although now a mature technology, FWI has limitations related to the choice of the appropriate solver for the forward problem in challenging environments requiring complex assumptions, and ve

Hui-Ru Ho, Nitigya Kargeti, Ziqi Liu, Bilge Mutlu

AI-assisted learning companion robots are increasingly used in early education. Many parents express concerns about content appropriateness, while they also value how AI and robots could supplement their limited skill, time, and energy to support their children's learning. We designed a card-based kit, SET, to systematically capture scenarios that have diffe

Cito Balsells, Beatrice Riviere, David Fuentes

We formulate truncated singular value decompositions of entire convolutional neural networks. We demonstrate the computed left and right singular vectors are useful in identifying which images the convolutional neural network is likely to perform poorly on. To create this diagnostic tool, we define two metrics: the Right Projection Ratio and the Left Project

Svea Hernandez, Linda J. Smith, Logan H. Jones, Aditya Togi

We report the first detections of [Ne V] 14.3 {\mu}m and [Ne VI] 7.7 {\mu}m at high confidence (S/N>=6) in the nuclear region of the nearby spiral galaxy M83. Emission line maps of these high ionization lines show several compact structures. Specifically, the [Ne VI] emission is located at 140 pc from the optical nucleus and appears as a point source of size

John C. Bodenschatz, Daniel B. Rowe

In the realm of neuroimaging research, the demand for efficient and accurate simulation tools for functional magnetic resonance imaging (fMRI) data is ever increasing. We present SHAKER, a comprehensive MATLAB package for simulating complex-valued fMRI time series data that will advance understanding and implementation of the MR signal equation and related p

Grant M. Kennedy

This paper describes a method for parametric radial profile modelling of radio interferometric visibility data. Image-based parametric modelling is common in the field of circumstellar debris disks, and high resolution ALMA observations make this method computationally expensive because many high resolution images must be generated and Fourier transformed. M

Sangwon Seo, Vaibhav Unhelkar

Successful collaboration requires team members to stay aligned, especially in complex sequential tasks. Team members must dynamically coordinate which subtasks to perform and in what order. However, real-world constraints like partial observability and limited communication bandwidth often lead to suboptimal collaboration. Even among expert teams, the same t

Elisabeta Lusso, Lapo Casetti, Marco Romoli, Lara Fossi

Active galactic nuclei (AGN) are known to be variable sources across the entire electromagnetic spectrum, in particular at optical/ultraviolet and X-ray energies. Over the past decades, a growing number of AGN have displayed type transitions: from type 1 to type 2 or viceversa within a few years or even several months. These galaxies have been commonly refer

Benedikt Buchecker, Benjamin Eichinger, Maxim Zinchenko

We study strong asymptotics of $L^r$-extremal polynomials for measures supported on Jordan regions with $C^{1+}$ boundary for $0<r<\infty$. Using the results for $r=2$, we derive asymptotics of weighted Chebyshev and residual polynomials for upper-semicontinuous weights supported on a $C^{1+}$ Jordan region corresponding to $r=\infty$. As an application, we

Fangshuo Liao, Wenyi Su, Anastasios Kyrillidis

We study a distributed Principal Component Analysis (PCA) framework where each worker targets a distinct eigenvector and refines its solution by updating from intermediate solutions provided by peers deemed as "superior". Drawing intuition from the deflation method in centralized eigenvalue problems, our approach breaks the sequential dependency in the defla

Stig Hellemans, Andres Algaba, Sam Verboven, Vincent Ginis

Counterfactual explanations provide actionable insights to achieve desired outcomes by suggesting minimal changes to input features. However, existing methods rely on fixed sets of mutable features, which makes counterfactual explanations inflexible for users with heterogeneous real-world constraints. Here, we introduce Flexible Counterfactual Explanations,

Taos Transue, Bao Wang

The orchestration of agents to optimize a collective objective without centralized control is challenging yet crucial for applications such as controlling autonomous fleets, and surveillance and reconnaissance using sensor networks. Decentralized controller design has been inspired by self-organization found in nature, with a prominent source of inspiration

Tianhui Zhang, Yi Zhou, Danushka Bollegala

Retrieval Augmented Generation (RAG) has gained popularity as a method for conveniently incorporating novel facts that were not seen during the pre-training stage in Large Language Model (LLM)-based Natural Language Generation (NLG) systems. However, LLMs are known to encode significant levels of unfair social biases. The modulation of these biases by RAG in

Samuel W. Yee, Gudmundur Stefansson, Daniel Thorngren, Andy Monson

The "super-puffs" are a population of planets that have masses comparable to that of Neptune but radii similar to Jupiter, leading to extremely low bulk densities ($\rho_p \lesssim 0.2\,\mathrm{g}\,\mathrm{cm}^{-3}$) that are not easily explained by standard core accretion models. Interestingly, several of these super-puffs are found in orbits significantly

Adrian Thummerer, Erik van der Bijl, Arthur Jr Galapon, Florian Kamp

Medical imaging is essential in modern radiotherapy, supporting diagnosis, treatment planning, and monitoring. Synthetic imaging, particularly synthetic computed tomography (sCT), is gaining traction in radiotherapy. The SynthRAD2025 dataset and Grand Challenge promote advancements in sCT generation by providing a benchmarking platform for algorithms using c

Marcelo V. Flamarion, Efim Pelinovsky, Ioann Melnikov

We investigate the interaction of solitons with an external periodic field within the framework of the modified Korteweg-de Vries (mKdV) equation. In the case of small perturbation a simple dynamical system is used to describe the soliton behaviour. Equilibrium points of this dynamical system are computed when the external force travels at a constant speed.

Christopher Zerafa, Pauline Galea, Cristiana Sebu

FWI seeks to achieve a high-resolution model of the subsurface through the application of multi-variate optimization to the seismic inverse problem. Although now a mature technology, FWI has limitations related to the choice of the appropriate solver for the forward problem in challenging environments requiring complex assumptions, and very wide angle and mu

Dang Nguyen, Zeman Li, Mohammadhossein Bateni, Vahab Mirrokni

Synthetic data has the potential to improve the performance, training efficiency, and privacy of real training examples. Nevertheless, existing approaches for synthetic text generation are mostly heuristics and cannot generate human-readable text without compromising the privacy of real data, or provide performance guarantees for training Large Language Mode

Viraj Thakkar, Qi Lin, Kenanya Keandra Adriel Prasetyo, Raden Haryosatyo Wisjnunandono

Log-Structured Merge-tree-based Key-Value Store (LSM-KVS) is a foundational storage engine serving diverse modern workloads, systems, and applications. To suit varying use cases, LSM-KVS allows a vast configuration space that controls core parameters like compaction, flush, and cache sizes, each consuming a shared pool of CPU, Memory, and Storage resources.

Tian Yu Liu, Alessandro Achille, Matthew Trager, Aditya Golatkar

Providing Large Language Models with relevant contextual knowledge at inference time has been shown to greatly improve the quality of their generations. This is often achieved by prepending informative passages of text, or 'contexts', retrieved from external knowledge bases to their input. However, processing additional contexts online incurs significant com

Rabimba Karanjai, Lei Xu, Weidong Shi

In this era, significant transformations in industries and tool utilization are driven by AI/Large Language Models (LLMs) and advancements in Machine Learning. There's a growing emphasis on Machine Learning Operations(MLOps) for managing and deploying these AI models. Concurrently, the imperative for richer smart contracts and on-chain computation is escalat

Luiz Emilio Allem, Carlos Hoppen, Lucas Siviero Sibemberg

The underlying graph $G$ of a symmetric matrix $M=(m_{ij})\in \mathbb{R}^{n\times n}$ is the graph with vertex set $\{v_1,\ldots,v_n\}$ such that a pair $\{v_i,v_j\}$ with $i\neq j$ is an edge if and only if $m_{ij}\neq 0$. Given a graph $G$, let $q(G)$ be the minimum number of distinct eigenvalues in a symmetric matrix whose underlying graph is $G$. A symme

Lukasz Bartoszcze, Sarthak Munshi, Bryan Sukidi, Jennifer Yen

Large-language models are capable of completing a variety of tasks, but remain unpredictable and intractable. Representation engineering seeks to resolve this problem through a new approach utilizing samples of contrasting inputs to detect and edit high-level representations of concepts such as honesty, harmfulness or power-seeking. We formalize the goals an

Zhongwei Wan, Hui Shen, Xin Wang, Che Liu

Long-context Multimodal Large Language Models (MLLMs) that incorporate long text-image and text-video modalities, demand substantial resources as their multimodal Key-Value (KV) caches grow with increasing input lengths, challenging inference efficiency. Existing methods for KV cache compression, in both text-only and multimodal LLMs, have neglected attentio

Jakub Binkowski, Denis Janiak, Albert Sawczyn, Bogdan Gabrys

Large Language Models (LLMs) have demonstrated remarkable performance across various tasks but remain prone to hallucinations. Detecting hallucinations is essential for safety-critical applications, and recent methods leverage attention map properties to this end, though their effectiveness remains limited. In this work, we investigate the spectral features

M. P. Bento, H. B. Câmara, J. F. Seabra

We parametrically solve the Boltzmann equations governing freeze-in dark matter (DM) in alternative cosmologies with Physics-Informed Neural Networks (PINNs), a mesh-free method. Through inverse PINNs, using a single DM experimental point -- observed relic density -- we determine the physical attributes of the theory, namely power-law cosmologies, inspired b

Santiago Guzmán-Pro, Barnaby Martin

In recent years, much attention has been placed on the complexity of graph homomorphism problems when the input is restricted to ${\mathbb P}_k$-free and ${\mathbb P}_k$-subgraph-free graphs. We consider the directed version of this research line, by addressing the questions, is it true that digraph homomorphism problems CSP$({\mathbb H})$ have a P versus NP

Bojana Brkić, Ilija Burić, Maja Burić, Dušan Đorđević

Quantum field theory on two- and three-dimensional fuzzy anti-de Sitter spaces is introduced and studied. We find a complete set of solutions to the fuzzy Klein-Gordon equation and identify the commutative limit in which they reduce to classical scalar field modes. After introducing the noncommutative boundary via semi-classical states, the fuzzy modes are u

Mahmoud Abdelshafy, Rubem Mondaini, Marcos Rigol

We show that in systems with highly degenerate energy spectra, such as the 2D transverse-field Ising model (2DTFIM) in the strong-field limit, quantum chaos can emerge in finite systems for arbitrary small perturbations. In this regime, the presence of extensive quasiconserved quantities can prevent finite systems from becoming ergodic. We study the ensuing

Fangqi Liu, Rishav Sen, Jose Paolo Talusan, Ava Pettet

Strategic aggregation of electric vehicle batteries as energy reservoirs can optimize power grid demand, benefiting smart and connected communities, especially large office buildings that offer workplace charging. This involves optimizing charging and discharging to reduce peak energy costs and net peak demand, monitored over extended periods (e.g., a month)

Damon Cleaver, Christopher McCabe, Ciaran A. J. O'Hare

Ultra-heavy dark matter candidates evade traditional direct detection experiments due to their low particle flux. We explore the potential of large underwater acoustic arrays, originally developed for ultra-high energy neutrino detection, to detect ultra-heavy dark matter interactions. These particles deposit energy via nuclear scattering while traversing se

Carlos Elihu Palomino Torres, Francisco Claudio Chichipe Mondragon, Frank Antonio Siesquen Rodriguez, Mariana Alexandra Huaynate Leon

This project presents the development of a real-time auditory enhancement system utilizing an ESP32, an LMS adaptive filter, and artificial intelligence techniques. An I2S INMP44 microphone captures the sound, which is dynamically processed to suppress noise before being played through a MAX98357 speaker. The system continuously adapts to varying acoustic en

Theodore Weisman

We generalize one part of Thurston's hyperbolic Dehn filling theorem to arbitrary-rank semisimple Lie groups by showing that certain deformations of extended geometrically finite subgroups of a semisimple Lie group are still extended geometrically finite. As a special case, our theorem gives a criterion which guarantees that a deformation of a relatively Ano

Martin Kuo, Jingyang Zhang, Jianyi Zhang, Minxue Tang

With the rise of large language models (LLMs), increasing research has recognized their risk of leaking personally identifiable information (PII) under malicious attacks. Although efforts have been made to protect PII in LLMs, existing methods struggle to balance privacy protection with maintaining model utility. In this paper, inspired by studies of amnesia

Keyu Zeng, Zhan Wang, Kun Jiang, Ziqiang Wang

The kagome metals $A$V$_3$Sb$_5$ ($A=$ K, Cs, Rb) have become a fascinating materials platform following the discovery of many novel quantum states due to the interplay between electronic correlation, topology, and geometry. Understanding their physical origin requires constructing effective theories that capture the low-energy electronic structure and elect

Raymond Choi, Frank Burns, Chase Lawrence

Automated chart summarization is crucial for enhancing data accessibility and enabling efficient information extraction from visual data. While recent advances in visual-language models (VLMs) have demonstrated promise, existing methods often suffer from limitations in matching the generated summary to the chart data and in reasoning about complex chart patt

Vanessa Yanza, Sergio A. Dzib, Aina Palau, Luis F. Rodríguez

New multi-wavelength Karl G. Jansky VLA observations of CKR02A, the compact radio source in the center of the compact HII region NGC 6334A, are presented. The observations were carried out in five epochs and included the frequency ranges 8.0 - 12.0 GHz (X-band), 18.0 - 26.0 GHz (K-band), and 29.0 - 37.0 GHz (Ka-band). The source is detected and resolved in a

Ilya G. Ryabinkin, Seyyed Mehdi Hosseini Jenab, Scott N. Genin

Immense interest in quantum computing has prompted development of electronic structure methods that are suitable for quantum hardware. However, the slow pace at which quantum hardware progresses, forces researchers to implement their ideas on classical computers despite the obvious loss of any "quantum advantage." As a result, the so-called quantum inspired

Issa Cherif Geraldo, Edoh Katchekpele, Tchilabalo Abozou Kpanzou

In statistics, processed data are becoming increasingly complex, and classical probability distributions are limited in their ability to model them. This is why, to better model data, extensive work has been conducted on extending classical probability distributions. Generally, this extension is achieved by transforming the cumulative distribution function o

Christopher Zerafa, Pauline Galea, Cristiana Sebu

This review explores the integration of deep learning (DL) with full-waveform inversion (FWI) for enhanced seismic imaging and subsurface characterization. It covers FWI and DL fundamentals, geophysical applications (velocity estimation, deconvolution, tomography), and challenges (model complexity, data quality). The review also outlines future research dire

Muzaffar Qureshi, Tochukwu Elijah Ogri, Humberto Ramos, Zachary I. Bell

This paper presents a methodology for an autonomous agent to map an unknown scalar field in GPS-denied regions. To reduce localization errors, the agent alternates between GPS-enabled and GPS-denied areas while collecting measurements. User-defined error bounds determine the dwell time in each region. A switching trajectory is then designed to ensure field m

Gebhard Boeckle, Peter Mathias Graef, Iason Papadopoulos

In this short note, we derive dimension formulas for spaces of Drinfeld cusp forms corresponding to harmonic cocycles invariant under the group $\mathrm{SL}_2(\mathbb{F}_q[t])$ and with values in absolutely irreducible $\mathrm{SL}_2(\mathbb{F}_q(t))$-representations via the theory of Brauer characters. This generalizes results in [BGP21] obtained by differe

Peijie Zhao, Zunayed Arefin, Felipe Meneguzzi, Ramon Fraga Pereira

In this demonstration, we develop IntentRec4Maps, a system to recognise users' intentions in interactive maps for real-world navigation. IntentRec4Maps uses the Google Maps Platform as the real-world interactive map, and a very effective approach for recognising users' intentions in real-time. We showcase the recognition process of IntentRec4Maps using two d

Scott J. Kenyon, Benjamin C. Bromley

We analyze a new set of 275 n-body calculations designed to place limits on the masses of the small circumbinary satellites in the Pluto-Charon system. Together with calculations reported in previous papers, we repeat that a robust upper limit on the total mass of the four satellites is roughly $9.5 \times 10^{19}$ g. For satellite volumes derived from New H

Rylan Schaeffer, Joshua Kazdan, John Hughes, Jordan Juravsky

Recent research across mathematical problem solving, proof assistant programming and multimodal jailbreaking documents a striking finding: when (multimodal) language model tackle a suite of tasks with multiple attempts per task -- succeeding if any attempt is correct -- then the negative log of the average success rate scales a power law in the number of att

P. Pietrukowicz, K. Werner, M. J. Mroz, M. Ratajczak

We show that the blue 18.3-minute variable object discovered in the Galactic disk by the OGLE-III survey and named OGLE-GD-WD-0001 is a pulsating pre-white dwarf of PG 1159 spectral type. With an effective temperature of about 160,000 K it is among the hottest known pulsators being located close to the blue edge of the GW Virginis instability strip. The long

Daniel Carney, Manthos Karydas, Thilo Scharnhorst, Roshni Singh

It was conjectured thirty years ago that gravity could arise from the entropic re-arrangement of information. In this paper, we offer a set of microscopic quantum models which realize this idea in detail. In particular, we suggest a simple mechanism by which Newton's law of gravity arises from extremization of the free energy of a collection of qubits or osc

Bishoy M. Kousa, Nicolás Morales-Durán, Tobias M. R. Wolf, Eslam Khalaf

The recent realization of Hofstadter spectra and fractional Chern insulators in moir\'e materials has introduced a new ingredient, a periodic lattice potential, to the study of quantum Hall phases. While the fractionalized states in moir\'e systems are expected to be in the same universality class as their counterparts in Landau levels, the periodic potentia

Jaime Hoefken Zink, Maura E. Ramirez-Quezada

We present a comprehensive theoretical framework describing single pion resonant production through inelastic dark fermion-nucleon interactions mediated by resonances in the GeV-scale regime. Building upon the Rein-Sehgal approach, we derive differential cross sections for processes in which an incoming dark fermion scatters off a nucleon, exciting a resonan

Osman Alperen Koraş, Rabi Bahnan, Jens Kleesiek, Amin Dada

Deploying natural language generation systems in clinical settings remains challenging despite advances in Large Language Models (LLMs), which continue to exhibit hallucinations and factual inconsistencies, necessitating human oversight. This paper explores automated dataset augmentation using LLMs as human proxies to condition LLMs for clinician control wit

Indrajit Ghose, Amitabha Lahiri

Neutrinos are excellent probes of the inner structures of supernovas. However, an understanding of their dynamics remains incomplete, which is crucial for properly interpreting the detector data. The huge matter density inside a core collapse supernova affects the self-coupling of the neutrinos through a geometrical four-fermion interaction induced by spacet

Nisarga Paul, Ahmed Abouelkomsan, Aidan Reddy, Liang Fu

We show that collective excitations and optical responses of moir\'e fractional Chern insulators (FCIs) drastically differ from those of standard fractional quantum Hall (FQH) states in a Landau level. By constructing a variational wavefunction that incorporates the moir\'e lattice effect, we capture the collective modes in FCIs across a range of crystal mom

Amanda Lue, Shy Genel, Marc Huertas-Company, Francisco Villaescusa-Navarro

Cosmological simulations like CAMELS and IllustrisTNG characterize hundreds of thousands of galaxies using various internal properties. Previous studies have demonstrated that machine learning can be used to infer the cosmological parameter $\Omega_m$ from the internal properties of even a single randomly selected simulated galaxy. This ability was hypothesi

Pouya M. Kouch, Elina Lindfors, Talvikki Hovatta, Ioannis Liodakis

It has been a decade since the IceCube collaboration began detecting high-energy (HE) neutrinos originating from cosmic sources. Despite a few well-known individual associations and numerous phenomenological, observational, and statistical multiwavelength studies, the origin of astrophysical HE neutrinos largely remains a mystery. To date, the most convincin

Masaru Shibata, Kyohei Kawaguchi, Alan Tsz-Lok Lam

We systematically perform long-term (millions of Schwarzschild time) axisymmetric viscous hydrodynamics simulations for tori around black holes in general relativity supposing the super Eddington accretion flow. The initial condition for the tori is modeled simply by the Fishbone-Moncrief torus with a constant specific angular momentum $j$ but with a wide va

Aidan DeBrae, Peter Behroozi, Nicolas Garavito-Camargo

High-velocity particles ( $v>v_\mathrm{esc}$) in the Milky Way are rare but nonetheless important to characterize due to their impact on dark matter (DM) direct detection experiments. We select halos similar in mass to the Milky Way in a large-volume dark matter simulation and measure the incidence of high-velocity particles, finding that an average fraction

Ross Glew, Tomasz Lukowski

The tree-level scattering amplitudes for $\text{tr}(\phi^3)$ theory can be interpreted as a sum over the vertices of a polytope known as the associahedron. For each graph $G$, there exists a natural generalisation of the associahedron, which is constructed by considering tubes and tubings of the underling graph. This family of polytopes are called graph asso

Amílcar R. Torres-Quijano, Natalie R. Hinkel, Caleb H. Wheeler, Patrick A. Young

Stars and their associated planets originate from the same cloud of gas and dust, making a star's elemental composition a valuable indicator for indirectly studying planetary compositions. While the connection between a star's iron (Fe) abundance and the presence of giant exoplanets is established (e.g. Gonzalez 1997; Fischer & Valenti 2005), the relationshi

Maria Demidik, Cenk Tüysüz, Nico Piatkowski, Michele Grossi

Quantum computers offer the potential for efficiently sampling from complex probability distributions, attracting increasing interest in generative modeling within quantum machine learning. This surge in interest has driven the development of numerous generative quantum models, yet their trainability and scalability remain significant challenges. A notable e

Claire E. Williams, Smadar Naoz, William Lake, Blakesley Burkhart

The James Webb Space Telescope (JWST) has opened a window on many new puzzles in the early Universe, including a population of high-redshift star clusters with extremely high stellar surface density, suggesting unique star formation conditions in the Universe's early evolution. We study the formation and evolution of these first star clusters and galaxies us

Leo P. Singer, Alexander W. Criswell, Sydney C. Leggio, R. Weizmann Kiendrebeogo

The UltraViolet EXplorer (UVEX) is a wide-field ultraviolet space telescope selected as a NASA Medium-Class Explorer (MIDEX) mission for launch in 2030. UVEX will undertake deep, cadenced surveys of the entire sky to probe low mass galaxies and explore the ultraviolet (UV) time-domain sky, and it will carry the first rapidly deployable UV spectroscopic capab

Beverly Lowell, Jonatan Jacquemin-Ide, Matthew Liska, Alexander Tchekhovskoy

Relativistic collimated outflows, or jets, provide a crucial mode of active galactic nucleus feedback. Although jets extract their energy from the black hole (BH) rotation, their effect on the BH spin is poorly understood. Because the spin controls radiative and mechanical BH feedback, lack of first-principles models for spin evolution limits our ability to

João Barata, Enrique Rico

In analogy to high-energy nuclear scattering experiments, we study a real-time scattering process between a propagating state and a dense target in $1+1$-d massive QED. In our setup, we identify three distinct regimes that qualitatively characterize the evolution: for a dilute medium, the incoming probe state evolves nearly ballistically; in an intermediate

Bernardo Barrera, Daniel P. Arovas, Anushya Chandran, Anatoli Polkovnikov

We develop a mixed quantum-classical framework, dubbed the Moving Born-Oppenheimer Approximation (MBOA), to describe the dynamics of slow degrees of freedom (DOFs) coupled to fast ones. As in the Born-Oppenheimer Approximation (BOA), the fast degrees of freedom adiabatically follow a state that depends on the slow ones. Unlike the BOA, this state depends on

Chiho Yoon, Tianyi Xu, Yafis Barlas, Fan Zhang

We investigate the recently discovered multiple superconducting states in rhombohedral graphene quarter metal. We demonstrate that one of these states features a single-spin, single-valley, single-band, single-Fermi-pocket parent state and is most likely a chiral topological pair-density wave, marked by a threefold symmetry that may not be spontaneously brok

Anubhav Srivastava, Stefan Birnkammer, GiBaik Sim, Michael Knap

Two-dimensional coherent spectroscopy (2DCS) is an established method for characterizing molecules and has been proposed in the THz regime as a new tool for probing exotic excitations of quantum magnets; however, the precise nature of the coupling between pump field and spin degrees of freedom has remained unclear. Here, we develop a general response theory

Silvan Fischbacher, Tomasz Kacprzak, Luis Fernando Machado Poletti Valle, Alexandre Refregier

Subhalo abundance matching (SHAM) is widely used for connecting galaxies to dark matter haloes. In SHAM, galaxies and (sub-)haloes are sorted according to their mass (or mass proxy) and matched by their rank order. In this work, we show that SHAM is the solution of the optimal transport (OT) problem on empirical distributions (samples or catalogues) for any

Tom Olsen, Luciano Rezzolla

We present the first extension of the special-relativistic Lattice-Boltzmann Method for radiative transport developed by Weih et al. (2020), to solve the radiative-transfer equation in curved spacetimes. The novel approach is based on the streaming of carefully selected photons along null geodesics and interpolating their final positions, velocities, and fre

Francesco Turro, Xiaojun Yao

We search for emergent hydrodynamic modes in real-time Hamiltonian dynamics of $2+1$-dimensional SU(2) lattice gauge theory on a quasi one dimensional plaquette chain, by numerically computing symmetric correlation functions of energy densities on lattice sizes of about $20$ with the local Hilbert space truncated at $j_{\rm max}=\frac{1}{2}$. Because of the

Qiaofeng Liu, Ian Low, Zhewei Yin

Magic is a quantum resource essential for universal quantum computation and represents the deviation of quantum states from those that can be simulated efficiently using classical algorithms. Using the Stabilizer R\'enyi Entropy (SRE), we investigate two-qubit states with maximal magic, which are most distinct from classical simulability, and provide strong

Ish Kaul, Brent Tan, S. Peng Oh, Nir Mandelker

The observed star formation and wind outflow rates in galaxies suggest cold gas must be continually replenished via infalling clouds or streams. Previous studies have highlighted the importance of cooling-induced condensation on such gas, which enables survival, mass growth, and a drag force which typically exceeds hydrodynamic drag. However, the combined ef

Bernhard Mistlberger, Gherardo Vita

We explore a factorization theorem for color singlet production cross sections at the LHC in the limit of additional radiation becoming collinear to the direction of either of the colliding protons. The resulting formula approximates the cross section as a function of the Born variables of the color singlet final state, specifically its mass and rapidity. We

Benjamin Lillard, Aria Radick

Anisotropic target materials are promising candidates for dark matter direct detection experiments, providing a directional sensitivity that can be used to distinguish a dark matter (DM) signal from the various Standard Model backgrounds. In this paper we introduce the Julia and Python implementations of \emph{Vector Spaces for Dark Matter} (VSDM), which han

Peter B. Denton, Charles Gourley

The discovery of solar neutrinos confirmed that the inner workings of the Sun generally match our theoretical understanding of the fusion process. Solar neutrinos have also played a role in discovering that neutrinos have mass and that they oscillate. We combine the latest solar neutrino data along with other oscillation data from reactors to determine the S

Seyed Faroogh Moosavian, Masahito Yamazaki, Yehao Zhou

We study the relation between spectral data of magnetic monopoles in hyperbolic space and the curve of the spectral parameter of generalized chiral Potts models (gCPM) through the lens of (semi-)holomorphic field theories. We realize the identification of the data on the two sides, which we call the hyperbolic monopole/gCPM correspondence. For the group $\te

Jelle Vandersnickt, Matthias Fabry

Context. Massive contact binaries are both stellar merger and gravitational wave progenitors, but their evolution is still uncertain. An open problem in the population synthesis of massive contact binaries is the predicted mass ratio distribution. Current simulations evolve quickly to mass ratios close to unity, which is not supported by the sample of observ

Louise Breuval, Caroline D. Huang, Adam G. Riess

Henrietta Swan Leavitt's discovery of the relationship between the period and luminosity (hereafter the Leavitt Law) of 25 variable stars in the Small Magellanic Cloud, published in 1912, revolutionized cosmology. These variables, eventually identified as Cepheids, became the first known "standard candles" for measuring extragalactic distances and remain the

Tianhong Li, Qinyi Sun, Lijie Fan, Kaiming He

Modularization is a cornerstone of computer science, abstracting complex functions into atomic building blocks. In this paper, we introduce a new level of modularization by abstracting generative models into atomic generative modules. Analogous to fractals in mathematics, our method constructs a new type of generative model by recursively invoking atomic gen

Yichi Zhang, Yici Yan, Alex Schwing, Zhizhen Zhao

We formulate a hierarchical rectified flow to model data distributions. It hierarchically couples multiple ordinary differential equations (ODEs) and defines a time-differentiable stochastic process that generates a data distribution from a known source distribution. Each ODE resembles the ODE that is solved in a classic rectified flow, but differs in its do

Chen-Wei Chang, Cheng-De Fan, Chia-Che Chang, Yi-Chen Lo

Color constancy methods often struggle to generalize across different camera sensors due to varying spectral sensitivities. We present GCC, which leverages diffusion models to inpaint color checkers into images for illumination estimation. Our key innovations include (1) a single-step deterministic inference approach that inpaints color checkers reflecting s

Hongyu Li, Mingxi Jia, Tuluhan Akbulut, Yu Xiang

Humans naturally integrate vision and haptics for robust object perception during manipulation. The loss of either modality significantly degrades performance. Inspired by this multisensory integration, prior object pose estimation research has attempted to combine visual and haptic/tactile feedback. Although these works demonstrate improvements in controlle

Bariscan Kurtkaya, Fatih Dinc, Mert Yuksekgonul, Marta Blanco-Pozo

Short-term memory is essential for cognitive processing, yet our understanding of its neural mechanisms remains unclear. Neuroscience has long focused on how sequential activity patterns, where neurons fire one after another within large networks, can explain how information is maintained. While recurrent connections were shown to drive sequential dynamics,