October 2025 arXiv papers — page 189

Alain Couvreur, Thomas Debris-Alazard, Philippe Gaborit, Adrien Vinçotte

We present $\mathsf{Miranda}$, the first family of full-domain-hash signatures based on matrix codes. This signature scheme fulfils the paradigm of Gentry, Peikert and Vaikuntanathan ($\mathsf{GPV}$), which gives strong security guarantees. Our trapdoor is very simple and generic: if we propose it with matrix codes, it can actually be instantiated in many ot

Maria Mahbub, Robert J. Klein, Myvizhi Esai Selvan, Rowena Yip

Lung cancer (LC) is the third most common cancer and the leading cause of cancer deaths in the US. Although smoking is the primary risk factor, the occurrence of LC in never-smokers and familial aggregation studies highlight a genetic component. Genetic biomarkers identified through genome-wide association studies (GWAS) are promising tools for assessing LC

Abraao J. S. Capistrano, Antonio C. Gutierrez-Pineres, Carlos H. Coimbra-Araujo

We investigate a rotating black hole embedded in a five-dimensional flat bulk by extending the Kerr metric through the Gurses-Gursey line element. Employing Boyer-Lindquist coordinates, we reinterpret the black hole as a charged-like object in five dimensions and analyze its horizon structure and shadow morphology. Our results reveal that the shadow is shape

Zheyuan Zhang, Lin Ge, Hongjiang Li, Weicheng Zhu

Large language models (LLMs) have demonstrated remarkable capabilities across diverse tasks, and LLM-based agents further extend these abilities to various practical workflows. While recent progress shows that multi-agent systems (MAS) can outperform single agents by coordinating specialized roles, designing effective MAS remains difficult due to prompt sens

Alex Kipnis, Marcel Binz, Eric Schulz

Hierarchical data with multiple observations per group is ubiquitous in empirical sciences and is often analyzed using mixed-effects regression. In such models, Bayesian inference gives an estimate of uncertainty but is analytically intractable and requires costly approximation using Markov Chain Monte Carlo (MCMC) methods. Neural posterior estimation shifts

Subash Adhikari, Riddhi Bandyopadhyay, Joshua Goodwill, William H. Matthaeus

Close to Earth the solar wind is usually super-Alfv\'enic, i.e. the speed of the solar wind is much larger than the Alfv\'en speed. However, in the lower coronal regions, the solar wind is mostly sub-Alfv\'enic. With the Parker Solar Probe (PSP) crossing the boundary between the sub- and super-Alfv\'enic flow, Bandyopadhyay et al. (2022) performed a turbulen

Wenhan Li, Shiyu Zhang

Long-distance quantum communication requires reliable entanglement distribution, but direct generation with protocols such as Barrett--Kok suffers from exponentially decreasing success probability with distance, making it impractical over hundreds of kilometers. Quantum repeaters address this by segmenting the channel and combining entanglement generation, s

Conor McCarthy, Loes Quirijnen, Jan Peter van Zandwijk, Zeno Geradts

Within Human Activity Recognition (HAR), there is an insurmountable gap between the range of activities performed in life and those that can be captured in an annotated sensor dataset used in training. Failure to properly handle unseen activities seriously undermines any HAR classifier's reliability. Additionally within HAR, not all classes are equally dissi

Marien Renaud, Julien Hermant, Deliang Wei, Yu Sun

Fast convergence and high-quality image recovery are two essential features of algorithms for solving ill-posed imaging inverse problems. Existing methods, such as regularization by denoising (RED), often focus on designing sophisticated image priors to improve reconstruction quality, while leaving convergence acceleration to heuristics. To bridge the gap, w

Pallav Goyal, Daniele Rosso

We show that the mirabolic quantum group $MU(n)$ is a comodule algebra over the quantized enveloping algebra $U_v(\mathfrak{sl}_n)$, and use this structure to give a complete classification of its finite dimensional representations. In particular, we explicitly describe the construction of all irreducible finite dimensional representations of $MU(n)$ and sho

Nils Siemonsen

Ultracompact spinning horizonless spacetimes with ergoregions are subject to the ergoregion instability. We systematically investigate the instability of a massless scalar field in a variety of rapidly spinning Proca stars and boson stars using WKB-, frequency-, and time-domain methods. We find universal features in the mode structure: the onset of the insta

Nils Siemonsen

We perform time-domain evolutions of the ergoregion instability on a horizonless spinning ultracompact spacetime in scalar theories with potential-type and derivative self-interactions mimicking the nonlinear structure of the Einstein equations. We find that the instability saturates by triggering a weakly turbulent direct cascade, which transfers energy fro

Hanwen Hu, Jiancheng An, Lu Gan, Naofal Al-Dhahir

Flexible intelligent metasurface (FIM) technology holds immense potential for increasing the spectral efficiency and energy efficiency of wireless networks. In contrast to traditional rigid reconfigurable intelligent surfaces (RIS), an FIM consists of an array of elements, each capable of independently tuning electromagnetic signals, while flexibly adjusting

Hanchen Wang, William Legrand, Davit Petrosyan, Min-Gu Kang

We report on the epitaxial growth of nm-thick films of bismuth-substituted yttrium iron garnet (BiYIG) by high-temperature off-axis radio-frequency magnetron sputtering. We demonstrate accurate control of the magnetic properties by tuning of the sputtering parameters and epitaxial strain on various (111)-oriented garnet substrates. BiYIG films with up to -0.

Waleed Bin Qaim, Oznur Ozkasap, Rabia Qadar, Moncef Gabbouj

The Internet of Things (IoT) bridges the gap between the physical and digital worlds, enabling seamless interaction with real-world objects via the Internet. However, IoT systems face significant challenges in ensuring efficient data generation, collection, and management, particularly due to the resource-constrained and unreliable nature of connected device

Z. Lin, S. Akhter, Z. Ahmad Dar, N. S. Alex

This paper illustrates a generic method for multi-dimensional reweighting of $O(1)$ GeV neutrino interaction Monte Carlo samples. The reweighting is based on a Boosted Decision Tree algorithm trained on high-dimensional space in detector final-state observables. This enables one generator's events to be reweighted so that its reconstructed particle content a

Joshua Hale, Theja N. De Silva

Driven by the growing demand in the energy, medical, and industrial sectors, we investigate a hydrogen isotope separation technique that offers both a high separation factor and economic feasibility. Our findings reveal that filtering isotopes through two-dimensional graphene layers provides an exceptionally efficient quantum-mechanical method for isotope se

Julian Stetzler, Sophya Garashchuk, Vitaly A. Rassolov

The Exact Factorization (XF) of molecular wavefunctions can be viewed as an 'electronic wavepacket' framework for quantum dynamics. It is an appealing alternative to the conventional non-adiabatic dynamics, unfolding in the space of coupled electronic eigenstates. However, implementation of the non-linear XF equations for general systems presents a formidabl

Gilad Aviv, Jacob Goldberger, Yoli Shavit

We introduce Mixture-of-Gaussians with Uncertainty-based Gating (MoGU), a novel Mixture-of-Experts (MoE) framework designed for regression tasks. MoGU replaces standard learned gating with an intrinsic routing paradigm where expert-specific uncertainty serves as the native gating signal. By modeling each prediction as a Gaussian distribution, the system util

Yujin J. Jung, Eduardo Ryô Tamaki, Julia Chatterley, Grant Mitchell

Measuring the ideational content of populism remains a challenge. Traditional strategies based on textual analysis have been critical for building the field's foundations and providing a valid, objective indicator of populist framing. Yet these approaches are costly, time consuming, and difficult to scale across languages, contexts, and large corpora. Here w

Kalyan Cheerla, Lotfi Ben Othmane, Kirill Morozov

Machine Learning (ML) is making its way into fields such as healthcare, finance, and Natural Language Processing (NLP), and concerns over data privacy and model confidentiality continue to grow. Privacy-preserving Machine Learning (PPML) addresses this challenge by enabling inference on private data without revealing sensitive inputs or proprietary models. L

Binrong Zhu, Guiran Liu, Nina Jiang

The application of advanced generative artificial intelligence in education is often constrained by the lack of real-time adaptability, personalization, and reliability of the content. To address these challenges, we propose ExpertAgent - an intelligent agent framework designed for personalized education that provides reliable knowledge and enables highly ad

Wim Ubachs, Frank M. J. Cozijn, Meissa L. Diouf, Clement Lauzin

In the early days of laser spectroscopy Letokhov and Chebotayev proposed a scheme for measuring narrow spectral lines where the resolution is not restricted to Doppler effects because the molecules are entrained in a standing-wave light field. Now, such one-dimensional trapping in the intensity maxima of an intracavity field, slightly detuned from resonance,

Karla Tame-Narvaez, Aleksandra Ćiprijanović, Steven Gardiner, Giuseppe Cerati

High-energy physics experiments studying neutrinos rely heavily on simulations of their interactions with atomic nuclei. Limitations in the theoretical understanding of these interactions typically necessitate ad hoc tuning of simulation model parameters to data. Traditional tuning methods for neutrino experiments have largely relied on simple algorithms for

Zifan Jiang, Colin Leong, Amit Moryossef, Anne Göhring

We present a comprehensive study on meaningfully evaluating sign language utterances in the form of human skeletal poses. The study covers keypoint distance-based, embedding-based, and back-translation-based metrics. We show tradeoffs between different metrics in different scenarios through automatic meta-evaluation of sign-level retrieval and a human correl

Shayan Mohajer Hamidi, En-Hui Yang, Ben Liang

Inverse problems, where the goal is to recover an unknown signal from noisy or incomplete measurements, are central to applications in medical imaging, remote sensing, and computational biology. Diffusion models have recently emerged as powerful priors for solving such problems. However, existing methods either rely on projection-based techniques that enforc

Anthony Hughes, Vasisht Duddu, N. Asokan, Nikolaos Aletras

Language models (LMs) may memorize personally identifiable information (PII) from training data, enabling adversaries to extract it during inference. Existing defense mechanisms such as differential privacy (DP) reduce this leakage, but incur large drops in utility. Based on a comprehensive study using circuit discovery to identify the computational circuits

Orfeu Bertolami

Speculation about the existence of advanced forms of life in the Universe and in our galaxy, has been since ever a subject of fascination and discussion in fiction, as well as in astrophysics, biology and philosophy. The well-known Fermi's 1950s challenge, "Where are the aliens?" has acquired more substance with the realisation of the potentialities of radio

Dimitrios Gousopoulos

Artificial Intelligence and especially Large Language Models (LLM), such as ChatGPT has revolutionized the way educators work. The results we get from LLMs depend on how we ask them to help us. The process and the technique behind an effective input is called prompt engineering. The aim of this study is to investigate whether science educators in secondary e

Vicente Saavedra-Araya

In this paper, we investigate ergodic and fractal properties of the sets $$\Lambda_y:=\Big\{n\in\mathbb{N}:\ \{u_ny\}\in I_n\Big\},$$ where $\{\cdot\}$ denotes the fractional part function, $(u_n)_{n\in\mathbb{N}}$ is an increasing sequence of real numbers, $y\in [0,1]$ and each $I_n$ is a finite union of intervals with decreasing Lebesgue measure. Our main

Georgia Antoniou, Haris Volos, Jawad Haj Yahya, Yiannakis Sazeides

This work introduces a model-based framework that reveals the idle opportunity of modern servers running latency-critical applications. Specifically, three queuing models, M/M/1, cxM/M/1, and M/M/c, are used to estimate the theoretical idle time distribution at the CPU core and system (package) level. A comparison of the actual idleness of a real server and

Carl Kristof-Tessier

We prove the Parallel Wall Theorem for CAT(0) 2-complexes constructed by regular polygons with an even number of sides. This result extends a combination of the works of Janzen and Wise and Hruska and Wise.

Nima Chartab, Andrew B. Newman, Gwen C. Rudie, Guillermo Blanc

We investigate the environmental dependence of galaxy properties at $z\sim2.5$ using the Ly$\alpha$ Tomography IMACS Survey (LATIS), which provides high-resolution three-dimensional maps of intergalactic medium (IGM) overdensity via Ly$\alpha$ forest tomography. Our analysis focuses on a UV-selected spectroscopic sample of 2185 galaxies from LATIS and a comp

Albert Di Wang, Ye Du

Risk management is a prominent issue in peer-to-peer lending. An investor may naturally reduce his risk exposure by diversifying instead of putting all his money on one loan. In that case, an investor may want to minimize the Value-at-Risk (VaR) or Conditional Value-at-Risk (CVaR) of his loan portfolio. We propose a low degree of freedom deep neural network

Peter J. Haine, Tim Holzschuh, Marcin Lara, Catrin Mair

We study a condensed version of the \'etale homotopy type of a scheme, which refines both the usual \'etale homotopy type of Friedlander-Artin-Mazur and the pro\'etale fundamental group of Bhatt-Scholze. In the first part of this paper, we prove that this condensed homotopy type satisfies descent along integral morphisms and that the expected fiber sequences

Nithin C. Babu, Aniruddha Mahapatra, Harsh Rangwani, Rajiv Soundararajan

Existing text-to-video (T2V) evaluation benchmarks, such as VBench and EvalCrafter, suffer from two limitations. (i) While the emphasis is on subject-centric prompts or static camera scenes, camera motion essential for producing cinematic shots and existing metrics under dynamic motion are largely unexplored. (ii) These benchmarks typically aggregate video-l

Dominik Woiwode, Jakob Marten, Bodo Rosenhahn

This paper presents a rotation-invariant embedded platform for simulating (neural) cellular automata (NCA) in modular robotic systems. Inspired by previous work on physical NCA, we introduce key innovations that overcome limitations in prior hardware designs. Our platform features a symmetric, modular structure, enabling seamless connections between cells re

Zhiyan Ding, Lin Lin, Yilun Yang, Ruizhe Zhang

Fine-grained spectral properties of quantum Hamiltonians, including both eigenvalues and their multiplicities, provide useful information for characterizing many-body quantum systems as well as for understanding phenomena such as topological order. Extracting such information with small additive error is $\#\textsf{BQP}$-complete in the worst case. In this w

M. Bordag, D. N. Voskresensky

Creation of charged spinless bosons from vacuum in the rigidly rotating frame is studied in presence of the external static electromagnetic fields, being formed either in the resting, or in the general rotating or in the local-flat frames. It is shown that the description remains the same in the resting and the local-flat frames. More specifically, the case

Amruta Parulekar, Preethi Jyothi

Standard ASR evaluation metrics like Word Error Rate (WER) tend to unfairly penalize morphological and syntactic nuances that do not significantly alter sentence semantics. We introduce an LLM-based scoring rubric LASER that leverages state-of-the-art LLMs' in-context learning abilities to learn from prompts with detailed examples. Hindi LASER scores using G

Ankur Naskar, Gugan Thoppe, Utsav Negi, Vijay Gupta

Federated learning (FL) can dramatically speed up reinforcement learning by distributing exploration and training across multiple agents. It can guarantee an optimal convergence rate that scales linearly in the number of agents, i.e., a rate of $\tilde{O}(1/(NT)),$ where $T$ is the iteration index and $N$ is the number of agents. However, when the training s

Zixuan Feng, Sadia Afroz, Anita Sarma

Generative AI (GenAI) is rapidly reshaping software development workflows. While prior studies emphasize productivity gains, the adoption of GenAI also introduces new pressures that may harm developers' well-being. In this paper, we investigate the relationship between the adoption of GenAI and developers' burnout. We utilized the Job Demands--Resources (JD-

Olia Toporkov, Alan Akbik, Rodrigo Agerri

Lemmatization is the task of transforming all words in a given text to their dictionary forms. While large language models (LLMs) have demonstrated their ability to achieve competitive results across a wide range of NLP tasks, there is no prior evidence of how effective they are in the contextual lemmatization task. In this paper, we empirically investigate

Kunal Pandey, Suhail Khan

In this article, we investigate the phenomenological aspects of a feebly interacting sterile neutrino dark matter candidate within a low-scale seesaw framework. The Type-I seesaw model is augmented by a second complex scalar doublet ($\Phi_{\nu}$), which couples exclusively to the heavy right-handed neutrinos and the lepton doublet, thereby generating the ne

Penghang Liu, Elizabeth Fons, Annita Vapsi, Mohsen Ghassemi

Large language models (LLMs) exhibit strong symbolic and compositional reasoning, yet they struggle with time series question answering as the data is typically transformed into an LLM-compatible modality, e.g., serialized text, plotted images, or compressed time series embeddings. Such conversions impose representation bottlenecks, often require cross-modal

Ann Marie Cody, Lynne A. Hillenbrand, Shreya Chandragiri, Marvin Morgan

We have carried out a photometric time domain study of 188 intermediate-mass young stars observed in Full Frame Image mode with the TESS satellite over the first 3.3 years of its mission. The majority of these targets are classified as Herbig Ae/Be stars (HAeBes). All were monitored at optical wavelengths for at least one 27-day TESS sector, with many having

Benius Dunn, Javier Meza-Arroyo, Armi Tiihonen, Mark Lee

Neuromorphic computing hardware enables edge computing and can be implemented in flexible electronics for novel applications. Metal oxide materials are promising candidates for fabricating flexible neuromorphic electronics, but suffer from processing constraints due to the incompatibilities between oxides and polymer substrates. In this work, we use photonic

Maxime Van de Moortel

In our recent work [Van de Moortel, The coexistence of null and spacelike singularities inside spherically symmetric black holes], we analyzed the transition between null and spacelike singularities in spherically symmetric dynamical black holes and demonstrated that the spacelike portion is described by a Kasner metric with positive varying exponents that d

Yang Jiao, Guanpu Chen, Yiguang Hong

In this paper, we study advanced persistent threats (APT) with an insider who has different preferences. To address the uncertainty of the insider's preference, we propose the BG-FlipIn: a Bayesian game framework for FlipIt-insider models with an investigation on malicious, inadvertent, or corrupt insiders. We calculate the closed-form Bayesian Nash Equilibr

Wang Wei, Tiankai Yang, Hongjie Chen, Yue Zhao

Efficient use of large language models (LLMs) is critical for deployment at scale: without adaptive routing, systems either overpay for strong models or risk poor performance from weaker ones. Selecting the right LLM for each query is fundamentally an online decision problem: models differ in strengths, prices fluctuate, and users value accuracy and cost dif

Lorraine Nicholson, Jaehan Bae

When a giant planet forms in a protoplanetary disks, it carves a gap around its orbit separating the disk into two parts: inner disk and outer disk. Traditional disk accretion models, which assume material transport is driven by viscosity, reveal that the planet-induced gap acts like a filter which blocks large dust grains from flowing into the inner disk. H

Matěj Hejda, Aishwarya Natarajan, Chaerin Hong, Mehmet Berkay On

Research into optical spiking neural networks (SNNs) has primarily focused on spiking devices, networks of excitable lasers or numerical modelling of large architectures, often overlooking key constraints such as limited optical power, crosstalk and footprint. We introduce SEPhIA, a photonic-electronic, multi-tiled SNN architecture emphasizing implementation

Walid Guettala, Yufan Zhao, László Gulyás

Traffic forecasting is fundamental to intelligent transportation systems, enabling congestion mitigation and emission reduction in increasingly complex urban environments. While recent graph neural network approaches have advanced spatial temporal modeling, existing mixture of experts frameworks like Time Enhanced Spatio Temporal Attention Model (TESTAM) lac

Pavel A. Andreev

The contribution of magnetoelectric effect to Landau--Lifshitz-Gilbert equation is considered in case when medium polarization is caused by parallel component of neighboring spins. The result is presented for ferromagnetic and antiferromagnetic materials. A comparison is given with the contribution of magnetoelectric effect to Landau--Lifshitz-Gilbert equati

Nathan Boyer, Dorian Baudry, Patrick Rebeschini

We study the problem of linear contextual bandits with paid observations, where at each round the learner selects an action in order to minimize its loss in a given context, and can then decide to pay a fixed cost to observe the loss of any arm. Building on the Follow-the-Regularized-Leader framework with efficient estimators via Matrix Geometric Resampling,

William Nguyen, Vinh Luong, Christopher Nguyen

Large language models (LLMs) have empowered AI agents to tackle increasingly complex tasks. However, most existing agents remain limited to static planning and brittle interactions, falling short of true collaboration or adaptive reasoning. We introduce ProSEA, a modular, general-purpose multi-agent framework designed for iterative problem solving through ex

Rémi Morvan

This thesis investigates the central role of homomorphism problems (structure-preserving maps) in two complementary domains: database querying over finite, graph-shaped data, and constraint solving over (potentially infinite) structures. Building on the well-known equivalence between conjunctive query evaluation and homomorphism existence, the first part foc

Javier Meza-Arroyo, Benius Dunn, Weijie Xu, Yu-Chieh Chen

Utilizing the intrinsic history-dependence and nonlinearity of hardware, physical reservoir computing is a promising neuromorphic approach to encode time-series data for in-sensor computing. The accuracy of this encoding critically depends on the distinguishability of multi-state outputs, which is often limited by suboptimal and empirically chosen reservoir

Guillem Domènech, Alexander Ganz, Apostolos Tsabodimos

Disformal couplings to fermions lead to a unique derivative coupling to the axial fermionic current, which contains higher derivatives in general. We derive general conditions on consistent disformal couplings by requiring the absence of higher time derivatives, as they typically lead to ghost degrees of freedom. For a two-scalar field disformal transformati

Jessica Yeh, Jinzhao Wang, Patrick Hayden

State merging is a fundamental protocol in quantum information theory that generalizes quantum teleportation. Traditionally, it is achieved by local operations on shared entanglement and classical communication. In this work, we study state merging done with alpha-bits, a versatile quantum communication resource weaker than qubits. We study alpha-bit state m

Corban Rivera, Grayson Byrd, Meghan Booker, Bethany Kemp

Coordinating heterogeneous robot teams from free-form natural-language instructions is hard. Language-only planners struggle with long-horizon coordination and hallucination, while purely formal methods require closed-world models. We present FLEET, a hybrid decentralized framework that turns language into optimized multi-robot schedules. An LLM front-end pr

Lucas Lagarde, Mohamed Moakher, Morena Porzio, James Rawson

Let $C$ be a curve of genus $g \geq 2$, and let $J$ be its Jacobian. The choice of a degree 1 divisor $e$ on $C$ gives an embedding of $C$ into $J$; we denote by $[C]_{}^{e}\in \mathrm{CH}\left( J;\mathbb{Q} \right) $ the class in the Chow group of $J$ defined by its image. It is known that the vanishing of the Ceresa cycle $\mathrm{Cer}(C,e):=[C]^{e} - [-1]

Prachurjo Dutta Roy

This paper discusses the history of Fraunhofer's puzzling discovery of the fixed lines in various spectra (most notably of the sun) and the implications of these spectral "imperfections". Moreover, the developments in spectroscopy by Kirchhoff, Bunsen, et al. in the 19th century and its effects on our understanding of the atomic structure are discussed.

Timothy L. Hutcheson, Anil K. Raj

We present a method for converting 24 channels of psychophysiologic time series data collected from individual participants via electroencephalogram (EEG), electrocardiogram (ECG), electrodermal activity (EDA), respiration rate (RR) into trackable three dimensional (3D) coordinates sufficient to estimate participation in specific task and cognitive states.

Mufei Li, Dongqi Fu, Limei Wang, Si Zhang

Modern long-context large language models (LLMs) perform well on synthetic "needle-in-a-haystack" (NIAH) benchmarks, but such tests overlook how noisy contexts arise from biased retrieval and agentic workflows. We argue that haystack engineering is necessary to construct noisy long contexts that faithfully capture key real-world factors -- distraction from h

Jun Liu

To overcome the bottleneck of classical path planning schemes in solving NP problems and address the predicament faced by current mainstream quantum path planning frameworks in the Noisy Intermediate-Scale Quantum (NISQ) era, this study attempts to construct a quantum path planning solution based on parallel Quantum Approximate Optimization Algorithm (QAOA)

J. D. Bondar, L. Banszerus, W. Marshall, T. Lindemann

We investigate Josephson arrays consisting of a dice-lattice network of superconducting weak links surrounding rhombic plaquettes of proximitized semiconductor. Josephson coupling of the weak links and electron density in the plaquettes are independently controlled by separate electrostatic gates. Applied magnetic flux results in an intricate pattern of swit

Anna Liptrott, Ricardo P. Schiavon, Andrew C. Mason, Sebastian Kamann

Liller 1 is a stellar system orbiting within the inner 0.8kpc of the Galactic centre, characterised by a wide spread in age and metallicity, indicating a high mass. Liller 1 has been proposed to be a major contributor to the stellar mass of the Galactic bulge, yet its origin is subject to debate. We employ Sloan Digital Sky Survey IV (SDSS-IV) data from the

Piotr Magierski

This review is based on lectures given by the author at the Enrico Fermi Summer School in Varenna. It presents the basics of Density Functional Theory (DFT) for Fermi superfluids, with particular emphasis on nuclear systems. Special attention is given to the foundations of both DFT and time-dependent DFT (TDDFT). The review explores the advantages and challe

Neil Natarajan, Sruthi Viswanathan, Xavier Roberts-Gaal, Michelle Marie Martel

Static solutions don't serve a dynamic mind. Thus, we advocate a shift from static mental health diagnostic assessments to continuous, artificial intelligence (AI)-driven assessment. Focusing on Attention-Deficit/Hyperactivity Disorder (ADHD) as a case study, we explore how generative AI has the potential to address current capacity constraints in neuropsych

Gregory W. Moore, Vivek Saxena

These are lecture notes expanding upon a set of lectures given by G.M. at the TASI 2023 School. Part I is an introduction to topological field theory, including extended topological field theory. Part II is an introduction to generalized Abelian gauge theories and their relation to differential cohomology.

Zoe A. Le Conte, Dimitri A. Gadotti, Leonardo Ferreira, Christopher J. Conselice

We investigate the evolution of the bar fraction and length using an extended JWST NIRCam imaging dataset of galaxies at $1 \leq z \leq 4$. We assess the wavelength dependence of the bar fraction and bar length evolution by selecting a nearly mass-complete CEERS disc sample and performing visual classifications on the short (F200W) and long (F356W+F444W) wav

Juan Daniel Torres Luna, Sebastian Miles, A. Mert Bozkurt, Chun-Xiao Liu

To observe Majorana bound states, and especially to use them as a qubit, requires careful optimization of competing quality metrics. We systematically compare Majorana quality in proximitized semiconductor nanowires and quantum dot chains. Using multi-objective optimization, we analyze the fundamental trade-offs between topological gap and localization lengt

Masoud Gharahi, Giorgio Ottaviani

Persistent tensors, introduced in [Quantum 8 (2024), 1238], and inspired by quantum information theory, form a recursively defined class of tensors that remain stable under the substitution method and thereby yield nontrivial lower bounds on tensor rank. In this work, we investigate the symmetric case-namely, symmetric persistent tensors, or equivalently, pe

L. Filipe O. Costa, José Natário

We show that, contrary to some recent claims, relativistic effects cannot mimic dark matter in the galactic rotation curves and gravitational lensing.

Emanuele Panella

The dynamics of hybrid systems -- i.e. ones in which classical and quantum degrees of freedom co-exist and interact -- feature both diffusion in the classical sector and decoherence in the quantum state. In this article, we will consider the simple setup of a classical damped oscillator interacting with its quantum counterpart and show that, for any initial

Şener Özönder

Phase transitions mark qualitative reorganizations of collective behavior, yet identifying their boundaries remains challenging whenever analytic solutions are absent and conventional simulations fail. Here we introduce learnability as a universal criterion, defined as the ability of a transformer model containing attention mechanism to extract structure fro

L. Filipe O. Costa, Francisco Frutos-Alfaro, José Natário, Michael Soffel

In a recent paper we discussed when it is possible to define reference frames nonrotating with respect to distant inertial reference objects (extension of the IAU reference systems to exact general relativity), and how to construct them. We briefly review the construction, illustrating it with further examples, and caution against the recent misuse of zero a

Pablo Martínez-Miravé, Irene Tamborra

Neutrinos are produced during stellar evolution by means of thermal and thermonuclear processes. We model the cumulative neutrino flux expected at Earth from all stars in the Milky Way: the Galactic stellar neutrino flux (GS$\nu$F). We account for the star formation history of our Galaxy and reconstruct the spatial distribution of Galactic stars by means of

Lasse Lorentz Braseth, Tore Klungland, Are Raklev

Near threshold, cross sections for the production of heavy particles are sensitive to large logarithmic terms, which must be resummed to all orders in perturbation theory. Current state-of-the art calculations for inclusive slepton pair production at hadron colliders has focused on higher-order logarithms in the leading power of the threshold variable. Here,

Thomas Hilder, Andrew R. Casey, Julianne J. Dalcanton, Kathryn Kreckel

Analyses of IFU data are typically performed on a per-spaxel basis, with each spectrum modelled independently. For low signal-to-noise (S/N) features such as weak emission lines, estimating properties is difficult and imprecise. Arbitrary binning schemes boost S/N at the cost of resolution, and risk introducing biases. We present a general forward-modelling

Camille Aron, Manas Kulkarni

The dynamics of extended many-body systems are generically chaotic. Classically, a hallmark of chaos is the exponential sensitivity to initial conditions captured by positive Lyapunov exponents. Supplementing chaotic dynamics with stochastic resetting drives a sharp dynamical phase transition: We show that the Lyapunov spectrum, i.e., the complete set of Lya

K. R. Neralwar, D. Colombo, S. Offner, A. Karska

Molecular clouds are active sites of star formation in galaxies, and their formation and evolution are largely affected by stellar feedback. This includes outflows and winds from newly formed stars, radiation from young clusters, and supernova explosions. High-resolution molecular line observations allow for the identification of individual star-forming regi

Bumhyun Lee, Min-Young Lee, Jungyeon Cho, Nickolas M. Pingel

Turbulence in the interstellar medium (ISM) plays an important role in many physical processes, including forming stars and shaping complex ISM structures. In this work, we investigate the HI turbulent properties of the Small Magellanic Cloud (SMC) to reveal what physical mechanisms drive the turbulence and at what scales. Using the high-resolution HI data o

Jeffrey D. Adler, Jessica Fintzen, Kazuma Ohara

Recently the authors have shown that every Hecke algebra associated to a type constructed by Kim and Yu is isomorphic to a Hecke algebra for a depth-zero type. An example in the literature has been suggested as a counterexample to this result. We show that the example is not a counterexample, and exhibit some of its interesting properties, e.g., we show that

Cristiano Ugolini

Intermediate-mass black holes (IMBHs) occupy the $ 10^2 - 10^5\,M_\odot $ range, but their existence remains poorly constrained. Only a few candidates have been suggested in dwarf galaxies, globular clusters, and LIGO-Virgo-Kagra detections. To investigate their formation and demographics, we adopt two complementary approaches. We first analyze the \textsc{d

Zhongjie Huang, Yichao Tang

We propose a definition of Mellin amplitudes for conformal correlators involving arbitrary spinning operators in tensor representations of the Lorentz group. These representations cover all bosonic local operators. Our strategy is to perform discrete Mellin transforms on all scalar products involving polarization vectors, so that each polarization vector can

R. Andrew Gustafson, Ian M. Shoemaker, Volodymyr Takhistov

Stars orbiting Sgr A* at the Milky Way's center provide a unique laboratory to test gravity and dark matter (DM). We demonstrate that DM interactions in stellar interiors induce a novel momentum transfer force, altering orbits beyond gravitational effects. Using S2's 2000-2019 orbital data we derive the first astrophysical constraints on DM-nucleon scatterin

J. P. McKean, C. Spingola, D. M. Powell, S. Vegetti

Compact symmetric objects (CSOs) are thought to be short-lived radio sources with two lobes of emission that are separated by less than a kpc in projection. However, studies of such systems at high redshift is challenging due to the limited resolution of present-day telescopes, and can be biased to the most luminous objects. Here we report imaging of a gravi

Gopal Chandra Santra, Julius Mildenberger, Edoardo Ballini, Alberto Bottarelli

Lattice gauge theories (LGTs) represent one of the most ambitious goals of quantum simulation. From a practical implementation perspective, non-Abelian theories present significantly tougher challenges than Abelian LGTs. However, it is unknown whether this is also reflected in increased values of quantum resources relating to the complexity of simulating qua

Georgios Palkanoglou, Alexandros Gezerlis

The gap equations lie at the core of the Bardeen-Cooper-Schrieffer (BCS) theory, a standard tool in the description of superfluidity. As a set of non-linear integral equations, the gap equations' inherent difficulties oftentimes hinder even the crudest descriptions of superfluid states. Hard-core potentials, high-density superfluids, and coupled-channel pair

D. M. Powell, J. P. McKean, S. Vegetti, C. Spingola

Structure on sub-galactic scales provides important tests of galaxy formation models and the nature of dark matter. However, such objects are typically too faint to provide robust mass constraints. Here, we report the discovery of an extremely low-mass object detected via its gravitational perturbation to a thin lensed arc observed with milli-arcsecond-resol

Daegwang Choi, Soon-Jae Lee, Seung-Woon Cho, Chan Bin Bark

From atomic crystals to macroscopic material structures, twisted bilayer systems have emerged as a promising route to control wave phenomena. In few-layer van der Waals (vdW) materials, however, the intrinsically weak interlayer coupling typically demands fine control of small twist angles to reach magic-angle conditions. Here, we show that one-dimensional p

Dominic W. Berry, Kianna Wan, Andrew D. Baczewski, Elliot C. Eklund

Here we describe an approach for simulating electronic structure on quantum computers with significantly lower asymptotic complexity than prior work. The approach uses a real-space first-quantised representation of the molecular Hamiltonian which we propagate using high-order product formulae. Essential for this low complexity is the use of a technique simil

Oscar J. C. Dias, Jorge E. Santos

We analyze the recently discovered localized and non-uniform phases of the Banks-Fischler-Shenker-Susskind (BFSS) matrix quantum mechanics. Building on [1], we provide first-principles derivations of their properties and extend the results with new analytic and numerical insights. We show that strongly coupled BFSS dynamics emerge from a specific Carrollian

R. Arora, S. Ranjan, P. Moitra, A. Mallik

H$_2$-dominated terrestrial exoplanets are highly accessible to atmospheric characterization via transmission spectroscopy, but such atmospheres are generally thought to be unstable to escape. Here, we propose that close-in, eccentric terrestrial exoplanets can sustain H$_2$-dominated atmospheres due to intense tidally-driven volcanic degassing. We develop a

Hao Chen, Pier Francesco Monni, Zhaoyan Pang, Gherardo Vita

In this Letter, we initiate a systematic study of the $n$-point correlation functions (CF) in gauge theories in the sequential light-cone (SLC) limit. Focusing on QCD, we formulate a factorization theorem for the CF of four vector currents in this limit using tools from soft-collinear effective field theory (SCET). This result unveils a duality between CF an

Brenda L. Jones, Dale D. Kocevski, Fabio Pacucci, Anthony J. Taylor

JWST has identified a large population of faint, broad-line active galactic nuclei (AGN) in the early universe that are powered by black holes (BHs) that often appear overmassive relative to their host galaxies. In this study, we examine the relationship between BH mass and galaxy stellar mass at $3<z<7$ using a sample of 70 broad-line AGN identified using N

Kai-Feng Chen, Meredith Neyer, Jacqueline N. Hewitt, Aaron Smith

Observations of the redshifted 21-cm line during the Epoch of Reionization will open a new window to probe the intergalactic medium during the formation of the first stars, galaxies, and black holes. A particularly promising route to an initial detection is to cross-correlate tomographic 21-cm maps with spectroscopically confirmed Lyman-$\alpha$ emitters (LA

Omri Lesser, Yanjun Liu, Natalie Maus, Aaditya Panigrahi

Predicting the superconducting transition temperature ($T_c$) from crystal structure and composition remains a central challenge in condensed-matter physics, reflecting the absence of a broadly predictive framework connecting microscopic bonding to macroscopic quantum behavior. Here, we introduce $\mathcal{GP}$-$T_c$, an interpretable, structure- and chemist