arXiv papers from December 2025

Miles Simmons, Ishan Bansal, Joe Cheriyan

Diestel, et al. (see Order 35 (2017), JCT-A 167 (2019), arXiv:1805.01439) introduced the notion of abstract separation systems that satisfy a submodularity property, and they call this structural submodularity. Williamson, Goemans, Mihail, and Vazirani (Combinatorica 15 (1995)) call a family of sets $\mathcal{F}$ uncrossable if the following holds: for any p

Sumaiya Ali, Areej Alhothali, Sameera Albasri, Ohoud Alzamzami

Placenta Accreta Spectrum (PAS) is a life-threatening obstetric complication involving abnormal placental invasion into the uterine wall. Early and accurate prenatal diagnosis is essential to reduce maternal and neonatal risks. This study aimed to develop and validate a deep learning framework that enhances PAS detection by integrating multiple imaging modal

Brady Zhou, Philipp Krähenbühl

Human drivers rarely travel where no person has gone before. After all, thousands of drivers use busy city roads every day, and only one can claim to be the first. The same holds for autonomous computer vision systems. The vast majority of the deployment area of an autonomous vision system will have been visited before. Yet, most autonomous vehicle vision sy

Jorge Ortiz

High-stakes deployment of vision-language models (VLMs) requires selective prediction, where systems abstain when uncertain rather than risk costly errors. We investigate whether confidence-based abstention provides reliable control over error rates in video question answering, and whether that control remains robust under distribution shift. Using NExT-QA a

Rachel Middleton, Ute Herzfeld, Thomas Trantow

We analyze the dynamics of Negribreen Glacier System, a polythermal glacier in Svalbard, during its ongoing surge and investigate the role of supraglacial (surface) water as both an indicator of ice-dynamic processes and a driver of surge evolution. We identify three distinct surge phases: the initial acceleration phase, mature phase, and return to quiescenc

Nan Miles Xi, Xin Huang, Lin Wang

Patients in clinical studies often exhibit heterogeneous treatment effect (HTE). Classical subgroup analyses provide inferential tools to test for effect modification, while modern machine learning methods estimate the Conditional Average Treatment Effect (CATE) to enable individual level prediction. Each paradigm has limitations: inference focused approache

Sam Chow, Zi Li Lim, Akshat Mudgal

Let $A$ be a sufficiently dense subset of a finite field $\mathbb F_q$ or a finite, cyclic ring $\mathbb Z/ N\mathbb Z$. Assuming that $q$ and $N$ have no small prime divisors, we show that generalised Fermat equations have the expected number of solutions over $A$. We further show that our density threshold is optimal. Our proofs involve average Fourier dec

Inpyo Song, Eunji Jeon, Jangwon Lee

Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains, including software development, education, and technical assistance. Among these, software development is one of the key areas where LLMs are increasingly adopted. However, when hardware constraints are considered-for instance, in physical computing, where software

Taegyu Pak, Dae Hee Wi, Sang Beom Kim, Jaewon Lim

We experimentally characterized terahertz (THz) radiation emitted from laser-wakefield acceleration (LWFA) driven at 100-TW laser power. Simultaneous measurements of the laser energy, electron-bunch charge, and THz energy reveal a quadratic dependence of the THz energy on both charge and laser energy. This behavior indicates coherent collective emission in t

Qinyang Li, Rongzhi Dong, Nicholas Miklaucic, Jeffrey Hu

Foundation models (FMs) have recently shown remarkable in-context learning (ICL) capabilities across diverse scientific domains. In this work, we introduce a unified in-context learning foundation model (ICL-FM) framework for materials property prediction that integrates both composition-based and structure-aware representations. The proposed approach couple

Alexey Basalaev

Saito theory associates to a quasihomogeneous isolated singularity the structure of a Dubrovin--Frobenius manifold. This structure is not unique, depending on the special choice of a primitive form or, equivalently, a good basis. We study primitive forms and respective Dubrovin--Frobenius manifolds via BV-algebras. In particular, we give recursive formulae f

Hongjian Zhou, Ziang Yin, Jiaqi Gu

Photonics is becoming a cornerstone technology for high-performance AI systems and scientific computing, offering unparalleled speed, parallelism, and energy efficiency. Despite this promise, the design and deployment of electronic-photonic AI systems remain highly challenging due to a steep learning curve across multiple layers, spanning device physics, cir

Ziang Yin, Hongjian Zhou, Nicholas Gangi, Meng Zhang

In this work, we identify three considerations that are essential for realizing practical photonic AI systems at scale: (1) dynamic tensor operation support for modern models rather than only weight-static kernels, especially for attention/Transformer-style workloads; (2) systematic management of conversion, control, and data-movement overheads, where multip

T. Rick Perche

This is an updated version of my PhD thesis, defended at the University of Waterloo on the 2nd of April 2025, uploaded to the ArXiv with the goal of reaching a wider audience. The thesis is divided into 5 chapters, respectively containing (I) a brief introduction to local quantum field theory (QFT), (II) a description of local probes in QFT, (III) a discussi

Ana M. Botti, Yikai Wu, Brenda Cervantes, Claudio Chavez

Skipper Charge-Coupled Devices (skipper-CCDs) are pixelated silicon detectors with deep sub-electron resolution. Their radiation hardness and capability to reconstruct energy deposits with unprecedented precision make them a promising technology for space-based X-ray astronomy. In this scenario, optical and near-infrared photons may saturate the sensor, dist

Utkarsh A Mishra, David He, Yongxin Chen, Danfei Xu

Generative models have emerged as powerful tools for planning, with compositional approaches offering particular promise for modeling long-horizon task distributions by composing together local, modular generative models. This compositional paradigm spans diverse domains, from multi-step manipulation planning to panoramic image synthesis to long video genera

Keqin Xie

Large Language Models (LLMs) exhibit persistent logical failures in complex reasoning due to the lack of an internal axiomatic framework. We propose Mathesis, a neuro-symbolic architecture that encodes mathematical states as higher-order hypergraphs and uses a Symbolic Reasoning Kernel (SRK)--a differentiable logic engine that maps constraints to a continuou

Aliza Sharmin, Bharat Sharma, Mustafa Can Camur, Olufemi A. Omitaomu

Resilient intermodal freight networks are vital for sustaining supply chains amid increasing threats from natural hazards and cyberattacks. While transportation resilience has been widely studied, understanding how random and targeted disruptions affect both structural connectivity and functional performance remains a key challenge. To address this, our stud

Hyunho Lee, Wenwen Li

Mapping water extent during a flood event is essential for effective disaster management throughout all phases: mitigation, preparedness, response, and recovery. In particular, during the response stage, when timely and accurate information is important, Synthetic Aperture Radar (SAR) data are primarily employed to produce water extent maps. Recently, levera

Eduardo Camps-Moreno, Jun Bo Lau, Hiram H. López, Welington Santos

In this work, we prove that the permutation group of a Reed-Solomon code is given by the polynomials of degree one that leave the set of evaluation points invariant. Our results provide a straightforward proof of the well-known cases of the permutation group of the Reed-Solomon code when the set of evaluation points is the whole finite field or the multiplic

Yaqi Duan, Yichun Hu, Jiashuo Jiang

Inventory management remains a challenge for many small and medium-sized businesses that lack the expertise to deploy advanced optimization methods. This paper investigates whether Large Language Models (LLMs) can help bridge this gap. We show that employing LLMs as direct, end-to-end solvers incurs a significant "hallucination tax": a performance gap arisin

Eliot Park, Abhi Kumar, Pranav Rajpurkar

While the importance of efficient recycling is widely acknowledged, accurately determining the recyclability of items and their proper disposal remains a complex task for the general public. In this study, we explore the application of cutting-edge vision-language models (GPT-4o, GPT-4o-mini, and Claude 3.5) for predicting the recyclability of commonly dispo

Hiram H. López, Gretchen L. Matthews, Daniel Valvo

A distributed storage system stores data across multiple nodes, with the primary objective of enabling efficient data recovery even in the event of node failures. The main goal of an exact repair scheme is to recover the data from a failed node by accessing and downloading information from the rest of the nodes. In a groundbreaking paper, ~\cite{GW} develope

Omer Angel, Caelan Atamanchuk, Anna Brandenberger, Serte Donderwinkel

We study the size and structure of the largest common subtree (LCS) between two independent Bienaym\'e trees conditioned to have size $n$. When the trees are critical with finite $2$nd and $(2+\kappa)$th moment respectively for some $\kappa>0$, we prove that the LCS has size of order $\sqrt{n}$, and is approximated by the length of three paths meeting at a c

Sergio Daniel Grillo

Let $\mathsf{E}$ be the event space of an experiment that can be indefinitely repeated. A natural question arises: given a countable cardinal $\kappa$, which is the event space of the $\kappa$-times repeated experiment? In the case of classical experiments, where $\mathsf{E}$ is a (complete) Boolean algebra on some set $S$, i.e. a classical or distributive l

Tyler D. Doležal, Rodrigo Freitas, Ju Li

Understanding how light interstitials migrate in chemically complex alloys is essential for predicting defect dynamics and long-term stability. Here, we introduce a spectral sampling framework to quantify boron diffusion activation energies in Ni and demonstrate how substitutional solutes (Cr, Mo) reshape interstitial point defect transport in both the bulk

Aditya Sai Ellendula, Yi Wang, Minh Nguyen, Chandrajit Bajaj

We present GRL-SNAM, a geometric reinforcement learning framework for Simultaneous Navigation and Mapping(SNAM) in unknown environments. A SNAM problem is challenging as it needs to design hierarchical or joint policies of multiple agents that control the movement of a real-life robot towards the goal in mapless environment, i.e. an environment where the map

Khalid T. Musri, Akram Y. Sarhan, Osamah A. Abdullah, Hayder Al-Hraishawi

This paper develops a gradient-based meta-learning framework for real-time control of waveguided pinching-antenna systems under user-location uncertainty and physical-layer security (PLS) constraints. A probabilistic system model is introduced to capture the impact of imperfect localization on outage performance and secrecy. Based on this model, a joint ante

Christoph Neuhauser

Differentiable rendering is a technique that aims to invert the rendering process to enable optimizing rendering parameters from a set of images. In this article, we present a differentiable volume rendering solution called DiffTetVR for tetrahedral meshes. Unlike previous works based on regular grids, this enables the optimization of vertex positions and th

Sherwin Kouchekian, Razvan Teodorescu

Despite being under intense scrutiny for 50 years, the Kuramoto oscillator model has remained a quintessential representative of non-equilibrium phase transitions. One of the reasons for its enduring relevance is the apparent lack of an optimization formulation, due to the fact that (superficially), the equations of motion seem to not be compatible with a La

Syed Mustafa Shah, Musawenkosi K. Ncube, Mohammed Lemaalem, Selva Chandrasekaran Selvaraj

Polymer electrolytes incorporating Li$_{10}$GeP$_{2}$S$_{12}$ (LGPS) nanoparticles show promise for solid-state lithium batteries owing to their enhanced ionic conductivity, though the governing mechanisms remain unclear. We combine molecular dynamics (MD) simulations, experimental ionic conductivity measurements, and density functional theory (DFT) calculat

J. Eisert

On physical grounds, one expects locally interacting quantum many-body systems to feature a finite group velocity. This intuition is rigorously underpinned by Lieb-Robinson bounds that state that locally interacting Hamiltonians with finite-dimensional constituents on suitably regular lattices always exhibit such a finite group velocity. This also implies th

Md Salik Parwez, Sai Teja Srivillibhutturu, Sai Venkat Reddy Kopparthi, Asfiya Misba

In this paper, we present \textit{CTMAP}, a large language model (LLM) empowered digital twin framework for connectivity-aware route navigation in millimeter-wave (mmWave) wireless networks. Conventional navigation tools optimize only distance, time, or cost, overlooking network connectivity degradation caused by signal blockage in dense urban environments.

Keiya Kawano, Milena Radenkovic

Tsunami disasters pose a serious and recurring threat to coastal and island communities. When a large earthquake occurs, people are forced to make evacuation decisions under extreme time pressure, often at the same time as the communication infrastructure is damaged or completely lost. In such circumstances, the familiar channels for sharing information - ce

Daniil S. Antonenko, Liang Fu, Leonid I. Glazman

We present the global topological phase diagram of a two-dimensional electron gas placed in a quantizing magnetic field and proximitized by a superconducting vortex lattice. Our theory allows for arbitrary ratios of the pairing amplitude, magnetic field, and chemical potential. By analyzing the Bogoliubov--de Gennes Hamiltonian, we show that the resulting ph

Deepyaman Chakraborty, Ruben Harris, Rupert Klein, Guillermo Olicón-Méndez

We introduce an affine invariant Langevin dynamics (ALDI) framework for the efficient estimation of rare events in nonlinear dynamical systems. Rare events are formulated as Bayesian inverse problems through a nonsmooth limit-state function whose zero level set characterises the event of interest. To overcome the nondifferentiability of this function, we pro

M. A. Chacón

This work examines the evolution of bar formation in disk galaxies over the last 6 giga-years by analyzing the barred galaxy fraction as a function of key structural parameters. A representative sample of local and distant field galaxies was studied using bar detection techniques based on elliptical isophotes and Fourier decomposition. The analysis focuses o

Muhammad U. Nasir, Yuchen Li, Steven James, Julian Togelius

We present Mortar, a system for autonomously evolving game mechanics for automatic game design. Game mechanics define the rules and interactions that govern gameplay, and designing them manually is a time-consuming and expert-driven process. Mortar combines a quality-diversity algorithm with a large language model to explore a diverse set of mechanics, which

Peter Borg

A copy of a hypergraph $F$ is called an $F$-copy. Let $K_k^r$ denote the complete $r$-uniform hypergraph whose vertex set is $[k] = \{1, \dots, k\}$ (that is, the edges of $K_k^r$ are the $r$-element subsets of $[k]$). Given an $r$-uniform $n$-vertex hypergraph $H$, the $K_k^r$-isolation number of $H$, denoted by $\iota(H, K_k^r)$, is the size of a smallest

Chenyang Amy Hu, David A. Meyer, Eleanor J. Q. Meyer

Aleksandrov, and then Zeeman, showed that the causal relations among the set of points in a Minkowski space of dimension greater than 2 determine the Minkowski space structure of the set up to a global conformal factor. We show that in any dimension the distances between causally related pairs of points determine the distances between spatially related pairs

Ari Stern, Enrico Zampa

The success of symplectic integrators for Hamiltonian ODEs has led to a decades-long program of research seeking analogously structure-preserving numerical methods for Hamiltonian PDEs. In this paper, we construct a large class of such methods by combining finite element exterior calculus (FEEC) for spatial semidiscretization with symplectic integrators for

Jarosław Arabas, Adam Stelmaszczyk, Eryk Warchulski, Dariusz Jagodziński

Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is a highly effective optimization technique. A primary challenge when applying CMA-ES in high dimensionality is sampling from a multivariate normal distribution with an arbitrary covariance matrix, which involves its decomposition. The cubic complexity of this process is the main obstacle to applying

Xiaoxuan Liu, Jiaxiang Yu, Jongseok Park, Ion Stoica

Speculative decoding (SD) has become a popular technique to accelerate Large Language Model (LLM) inference, yet its real-world effectiveness remains unclear as prior evaluations rely on research prototypes and unrealistically small batch sizes. We present, to our knowledge, the first systematic study of SD on a production-grade and widely deployed inference

Matthew Nyaaba, Patrick Kyeremeh, Macharious Nabang, Bismark Nyaaba Akanzire

Global frameworks increasingly advocate for Responsible Artificial Intelligence (AI) in education, yet they provide limited guidance on how ethical, culturally responsive, and curriculum-aligned AI can be operationalized within functioning teacher education systems, particularly in the Global South. This study addresses this gap through the design and evalua

T. Poulis, G. Mani, J. Sturt, W. J. Duncan

In the metallic magnet NbFe2, the low temperature threshold of ferromagnetism can be investigated by varying the Fe concentration within a narrow homogeneity range. NbFe2 is one of a number of compounds where modulated order is found to mask the ferromagnetic quantum critical point. However, here we report the rare case where the masking modulated magnetic o

Sung-Lin Yeh, Peter Bell, Hao Tang

Despite being the best known objective for learning speech representations, the HuBERT objective has not been further developed and improved. We argue that it is the lack of an underlying principle that stalls the development, and, in this paper, we show that predictive coding under a variational view is the principle behind the HuBERT objective. Due to its

Umesh Dhumal

Understanding how interparticle interactions govern phase behavior is central to controlling self-organization in multicomponent soft-matter systems. In particular, the role of cross-interactions between unlike components remains insufficiently understood. Here, we systematically investigate how cross-interaction character controls phase behavior in binary m

Paraschos Koutris, Stijn Vansummeren, Qichen Wang, Yisu Remy Wang

Yannakakis' seminal algorithm is optimal for acyclic joins, yet it has not been widely adopted due to its poor performance in practice. This paper briefly surveys recent advancements in making Yannakakis' algorithm more practical, in terms of both efficiency and ease of implementation, and points out several avenues for future research.

Akash Kumar Panda, Olaoluwa Adigun, Bart Kosko

We design a large-language-model (LLM) agent system that extracts causal feedback fuzzy cognitive maps (FCMs) from raw text. The causal learning or extraction process is agentic both because of the LLM's semi-autonomy and because ultimately the FCM dynamical system's equilibria drive the LLM agents to fetch and process causal text. The fetched text can in pr

Ignacio Morales-Gil

RS Oph is a recurrent nova, a kind of cataclismic variable that shows bursts in a period approximately shorter than a century. Persistent homology, a technique from topological data analysis, studies the evolution of topological features of a simplicial complex composed of the data points or an embedding of them, as some distance parameter is varied. For thi

Ahmed Sheta, Andrew Strominger, Adam Tropper, Hongji Wei

Flat Minkowski space (M$^4$) and AdS$_4$ can both be conformally mapped to the Einstein cylinder. The maps may be judiciously chosen so that some null generators of the $\mathcal{I}^+$ boundary of M$^4$ coincide with antipodally-terminating null geodesic segments on the boundary of AdS$_4$. Conformally invariant nonabelian gauge theories in M$^4$ have an asy

Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma

Large language models increasingly require structured inference, from JSON schema enforcement to multi-lingual parsing, where outputs must satisfy complex constraints. We introduce MetaJuLS, a meta-reinforcement learning approach that learns universal constraint propagation policies applicable across languages and tasks without task-specific retraining. By f

Ali Dasdan

The problem of finding the longest simple cycle in a directed graph is NP-hard, with critical applications in computational biology, scheduling, and network analysis. Existing approaches include exact algorithms with exponential runtimes, approximation algorithms limited to specific graph classes, and heuristics with no formal guarantees. In this paper, we e

Daewon Lee, Sam Oaks-Leaf, Hyeonjong Ma, Jianlong He

Pathways and structural dynamics of phase transformations impact performance of materials in energy and information storage technologies. Palladium hydride ($\mathrm{PdH}_x$) nanocrystals are an ideal model system for studying solute-induced phase transformations, where elastic energy from lattice mismatch between $\alpha$-$\mathrm{PdH}_x$ and $\beta$-$\math

Muhammad Aurangzeb Ahmad

Large Language Models (LLMs) are rapidly transforming how communities access, interpret, and circulate knowledge, and religious communities are no exception. Chatbots powered by LLMs are beginning to reshape authority, pedagogy, and everyday religious practice in Muslim communities. We analyze the landscape of LLM powered Islamic chatbots and how they are tr

Pan Wang, Yang Liu, Guile Wu, Eduardo R. Corral-Soto

4D spatial intelligence involves perceiving and processing how objects move or change over time. Humans naturally possess 4D spatial intelligence, supporting a broad spectrum of spatial reasoning abilities. To what extent can Multimodal Large Language Models (MLLMs) achieve human-level 4D spatial intelligence? In this work, we present Spatial4D-Bench, a vers

Manuel Sáenz, Pragya Sur

We investigate Bayes posterior distributions in high-dimensional generalized linear models (GLMs) under the proportional asymptotics regime, where the number of features and samples diverge at a comparable rate. Specifically, we characterize the limiting behavior of finite-dimensional marginals of the posterior. We establish that the posterior does not contr

Anne Harrington, A. Sophia Koepke, Shyamgopal Karthik, Trevor Darrell

Contemporary text-to-image models exhibit a surprising degree of mode collapse, as can be seen when sampling several images given the same text prompt. Previous work has attempted to address this issue by steering the model using guidance mechanisms, or by generating a large pool of candidates and refining them. In this work, we take a different direction an

David D Vaida, Ryan Jeffrey Farber

Since the launch of James Webb Space Telescope (JWST) in late 2021, our understanding of high-redshift objects has faced several upheavals. JWST has discovered much more massive galaxies and supermassive black holes (SMBH) than cosmological models had expected. Furthermore, JWST observations have revealed an entirely novel population of high-redshift objects

Junqi Qu, Yan Zhang, Shangqian Gao, Shibo Li

Large Language Models (LLMs) show promise for equation discovery, yet their outputs are highly sensitive to prompt phrasing, a phenomenon we term instruction brittleness. Static prompts cannot adapt to the evolving state of a multi-step generation process, causing models to plateau at suboptimal solutions. To address this, we propose NeuroSymBO, which refram

Qiang Li, Shujian Yu, Liang Ma, Chen Ma

Blind source separation, particularly through independent component analysis (ICA), is widely utilized across various signal processing domains for disentangling underlying components from observed mixed signals, owing to its fully data-driven nature that minimizes reliance on prior assumptions. However, conventional ICA methods rely on an assumption of line

Zhaoan Wang, Junchao Li, Mahdi Mohammad, Shaoping Xiao

Robotic systems operating in dynamic and uncertain environments increasingly require planners that satisfy complex task sequences while adhering to strict temporal constraints. Metric Interval Temporal Logic (MITL) offers a formal and expressive framework for specifying such time-bounded requirements; however, integrating MITL with reinforcement learning (RL

Joel Franklin, David Griffiths, Darrell Schroeter

Contrary to widespread belief, magnetostatic field lines do not ordinarily form closed loops. Why, then, are they in fact closed for so many familiar examples? What other topologies are possible, and what current configurations generate them?

Asha Ann Abraham, Anjali Kumari, Md Aktar Hossain, Sanjoy Kr Mahatha

Ruthenates, materials with a single magnetic Ruthenium (Ru) atom, often display an exotic array of ground states ranging from superconductivity to altermagnetism. In this work, we investigated the magnetic ground state of a least explored member of the 4d3 double perovskite ruthenate series A2ScRuO6 (A = Ca, Sr, Ba): Ca2ScRuO6. Interestingly, temperature-dep

Brian M. Cho, Nathan Kallus

In fixed-confidence best arm identification (BAI), the objective is to quickly identify the optimal option while controlling the probability of error below a desired threshold. Despite the plethora of BAI algorithms, existing methods typically fall short in practical settings, as stringent exact error control requires using loose tail inequalities and/or par

J. Sumaya-Martinez, J. Mulia-Rodriguez

Near-cloaks based on passive coatings can strongly suppress scattered-field energy in a narrow frequency band, yet an observer's ability to infer object parameters from noisy measurements need not decrease proportionally. We develop a fully theoretical two-dimensional (2D) framework for a coated acoustic cylinder in an air background. Using an exact cylindri

Leticia Guedes, Gabriela Hoff, Farinaldo S. Queiroz, Y. M. Oviedo-Torres

Doubly charged scalars frequently emerge in many well-motivated extensions of the Standard Model, particularly in frameworks that aim to explain the origin of neutrino masses. Their distinct electric charge and clean leptonic signatures make them especially compelling from the standpoint of experimental searches. In this work, we explore the sensitivity of t

Farinaldo S. Queiroz, J. Zamora-Saa, Ricardo C. Silva, Y. M. Oviedo-Torres

We present a phenomenological study of the discovery potential at the FCC-hh for a new heavy neutral vector boson, Z', predicted by the $U(1)_{B-L}$ gauge symmetry. Focusing on the parameter space currently not excluded by Large Hadron Collider data, we analyze the dilepton production channel $p p \rightarrow Z^{\prime} \rightarrow l^{+} l^{-}$ ($l^{\pm} = e

Henry Crandall, Tyler Schuessler, Filip Bělík, Albert Fabregas

Wearable technologies have the potential to transform ambulatory and at-home hemodynamic monitoring by providing continuous assessments of cardiovascular health metrics and guiding clinical management. However, existing cuffless wearable devices for blood pressure (BP) monitoring often rely on methods lacking theoretical foundations, such as pulse wave analy

Yazeed Tawalbeh, Mauro F. Pereira

We examine the reduction of silver nanoparticle (AgNP) size under an external magnetic field within a classical nucleation theory framework combined with a sphere-packing description of atomic assembly. The model incorporates magnetic free-energy contributions arising from the coupling between the applied field and the magnetic susceptibility of the nucleati

Gerardo Urrutia, Agnieszka Janiuk

Long gamma-ray bursts (lGRB) are produced by relativistic jets arising from the collapse of massive stars. Such progenitor environments present complex physical conditions that are challenging to model by numerical simulations. The difficulty increases when solving the accretion process and propagation of the outflows, as it requires covering distances from

Nicolas Zapata, Najmeh Etehadi Abari, Mitchell Field, Patrick Winkel

Superconducting standing$-$wave parametric amplifiers are crucial for the readout of microwave quantum devices. Despite significant improvements in recent years, the need to operate near an instability point imposes a fundamental constraint: the instantaneous bandwidth decreases with increasing amplifier gain. Here we show that it is possible to obtain param

Tailan S. Sarubi, Santiago Zamora, Moisés Alves, Vinícius F. Alves

This article provides a comprehensive review of the critical role of detection efficiency in demonstrating non-classicality across various device-independent and semi-device-independent scenarios. The central focus is the detection loophole, a challenge in which imperfect detectors can allow classical hidden variable models to mimic quantum correlations, thu

Swetha Varadarajan, Abhishek Ray, Lumina Albert

Illicit Massage Businesses (IMBs) are a covert and persistent form of organized exploitation that operate under the facade of legitimate wellness services while facilitating human trafficking, sexual exploitation, and coerced labor. Detecting IMBs is difficult due to encoded digital advertisements, frequent changes in personnel and locations, and the reuse o

Francesco Fournier-Facio, Roman Sauer

We construct a family of simple, lacunary hyperbolic groups with property $(T)$ that have rational cohomological dimension~$16$ and whose second $\ell^2$-Betti number can be prescribed to be any positive real. Moreover, we construct hyperbolic groups with property $(T)$ whose second $\ell^2$-Betti number can be prescribed to be any non-negative rational. Alo

Md Anisur Rahman, Md Asif Hasan Khan, Tuan Mai, Jinki Kim

Bioprinting technology has advanced significantly in the fabrication of tissue-like constructs with complex geometries for regenerative medicine. However, maintaining the structural integrity of bioprinted materials remains a major challenge, primarily due to the frequent and unexpected formation of hidden defects. Traditional defect detection methods often

Nicholas Z. Rui, Jim Fuller

Once carbon--oxygen white dwarfs cool sufficiently, they crystallize from the inside out. If the white dwarf is rich enough in ${}^{22}\mathrm{Ne}$, these crystallized solids are buoyant and rapidly rise, efficiently liberating potential energy which may halt the cooling of the white dwarf or power magnetic phenomena. Although this ${}^{22}\mathrm{Ne}$ disti

Martin Rainer

Newtonian gravity arises as the nonrelativistic, static, weak-field limit of some Lorentzian spacetime geometry solving the generally covariant Einstein equations for a given matter field configuration. Spacetime geometry has a local description in the spinor basis of Penrose. The breakdown of relativistic quantum (field) theory at small distances suggests t

Reuven Balkin, Ta'el Coren, Alexander Jentsch, Hongkai Liu

We investigate the sensitivity of the Electron-Ion Collider (EIC) to invisible final states in coherent exclusive electroproduction. The characteristic signal is a forward proton with reduced energy and little additional detector activity. Using the excellent particle detection capabilities and kinematics reconstruction at the EIC, we argue that backgrounds

Merritt P. R. Losert, Dario Denora, Barnaby van Straaten, Michael Chan

As quantum dot (QD)-based spin qubits advance toward larger, more complex device architectures, rapid, automated device characterization and data analysis tools become critical. The orientation and spacing of transition lines in a charge stability diagram (CSD) contain a fingerprint of a QD device's capacitive environment, making these measurements useful to

Martin A. Mojahed, Sascha Weber

We present an experimentally testable leptogenesis mechanism based on the standard type-I seesaw model that successfully operates at right-handed-neutrino (RHN) masses around the GeV scale. The mechanism takes place in a cosmological background with an asymmetry between right-handed electrons and left-handed positrons generated at high temperatures, and does

Haowei Fan, Vladimir Fal'ko, Xiao Li

We study a periodically driven macrospin system with anisotropic long-range interactions and collective dissipation, described by a Lindblad master equation. In the thermodynamic limit ($N\to\infty$), a mean-field treatment yields classical equations of motion, whose dynamics are characterized via the maximal Lyapunov exponent (MLE). Focusing on the thermody

W. H. Elsanhoury, S. Taşdemir, D. C. Çınar, R. Canbay

We present a comprehensive analysis of four young open clusters, NGC 663, NGC 2301, NGC 2384, and NGC 7510, utilizing high-precision astrometric and photometric data from Gaia DR3. Cluster membership was determined using the UPMASK algorithm, resulting in probable member counts ranging from 337 to 1498 across the clusters. Bayesian MCMC isochrone fitting yie

Qicheng Zhang, Quanzhi Ye, Karl Battams, Matthew M. Knight

On 2018 November 18, coronagraphs onboard the Solar and Heliospheric Observatory (SOHO) captured an unrecognized comet crossing its fields of view. We identified this comet to be the minor planet (139359) 2001 ME1 whose previously unnoticed dust activity near perihelion became optically amplified by efficient forward scattering of sunlight as the comet cross

Roberto Tejada Arevalo, Akash Gupta, Adam Burrows, Donghao Zheng

We explore the evolution of sub-Neptune (radii between $\sim$1.5 and 4 R$_\oplus$) exoplanet interior structures using our upgraded evolution code, \texttt{APPLE}, which self-consistently couples the thermal and compositional evolution of the whole structure. We incorporate stably stratified regions with convective mixing and, for the first time, ab initio r

Leonardo Rastelli, Brandon C. Rayhaun, Matteo Sacchi, Gabi Zafrir

Motivated by the observation that $2+2=4$, we consider four-dimensional $\mathcal{N}=2$ superconformal field theories on $S^2\times\Sigma$, turning on a suitable rigid supergravity background. On the one hand, reduction of a four-dimensional theory ${T}$ on a Riemann surface $\Sigma$ leads to a family $\mathscr{F}[{T}, \Sigma]$ of two-dimensional $(2,2)$ uni

Philipp Hake, Matthias Keller, Felix Pogorzelski

We study the fractional Hardy inequality on the integer lattice. We prove null-criticality of the Hardy weight and hence optimality of the constant. More specifically, we present a family of Hardy weights with respect to a parameter and show that below a certain threshold the Hardy weight is positive critical while above the threshold it is subcritical. In p

Subo Dong, Zexuan Wu, Yoon-Hyun Ryu, Andrzej Udalski

A population of free-floating planets is known from gravitational microlensing surveys. None have a directly measured mass, owing to a degeneracy with the distance, but the population statistics indicate that many are less massive than Jupiter. We report a microlensing event -- KMT-2024-BLG-0792/OGLE-2024-BLG-0516, which was observed from both ground- and sp

Nikolay V. Gnezdilov, Andrei I. Pavlov

The emergence of statistical mechanics from quantum dynamics is a central problem in quantum many-body physics. Deriving observables aligned with the prediction of the canonical ensemble for a quantum system relies on the presence of a bath provided either as an external environment or as a larger part of a closed system. We demonstrate that thermal (canonic

Sumit K. Sarbadhicary, David Thilker, Adam K. Leroy, Janice C. Lee

Red supergiants (RSGs) are important for our understanding of supernova progenitors, stellar populations, stellar evolution, mass loss and dust production. Extragalactic surveys of RSGs have a long history in the Local Group, but few studies exist beyond that due to the limited resolution and sensitivity of ground-based and previous space-based infrared obse

Renpeng Zheng

We study the K-stability of $\mathbb{Q}$-Fano spherical varieties using compatible divisors. More precisely, if the $\mathbb{Q}$-Fano variety, with a reductive group action, has an open Borel subgroup orbit, then there is a unique anticanonical $\mathbb{Q}$-divisor computing the equivariant stability threshold. This $\mathbb{Q}$-divisor is invariant under th

Zhening Huang, Hyeonho Jeong, Xuelin Chen, Yulia Gryaditskaya

We present SpaceTimePilot, a video diffusion model that disentangles space and time for controllable generative rendering. Given a monocular video, SpaceTimePilot can independently alter the camera viewpoint and the motion sequence within the generative process, re-rendering the scene for continuous and arbitrary exploration across space and time. To achieve

Souradeep Ghosh, Nicholas Hunter-Jones, Joaquin F. Rodriguez-Nieva

Randomness generation through quantum-chaotic evolution underpins foundational questions in statistical mechanics and applications across quantum information science, including benchmarking, tomography, metrology, and demonstrations of quantum computational advantage. While statistical mechanics successfully captures the temporal averages of local observable

Yi-Chuan Huang, Hao-Jen Chien, Chin-Yang Lin, Ying-Huan Chen

Recent 3D reconstruction methods achieve impressive results with dense multi-view imagery but struggle when only a few views are available. Various approaches, including regularization techniques, semantic priors, and geometric constraints, have been implemented to address this challenge. Recent diffusion-based approaches further improve performance by gener

Haozhi Qi, Yen-Jen Wang, Toru Lin, Brent Yi

Humanoid robots hold great promise for operating in human-centric environments, yet achieving robust whole-body coordination across the head, hands, and legs remains a major challenge. We present a system that combines a modular teleoperation interface with a scalable learning framework to address this problem. Our teleoperation design decomposes humanoid co

Jiageng Liu, Weijie Lyu, Xueting Li, Yejie Guo

We present Edit3r, a feed-forward framework that reconstructs and edits 3D scenes in a single pass from unposed, view-inconsistent, instruction-edited images. Unlike prior methods requiring per-scene optimization, Edit3r directly predicts instruction-aligned 3D edits, enabling fast and photorealistic rendering without optimization or pose estimation. A key c

Nikhil Chandak, Shashwat Goel, Ameya Prabhu, Moritz Hardt

High-stakes decision making involves reasoning under uncertainty about the future. In this work, we train language models to make predictions on open-ended forecasting questions. To scale up training data, we synthesize novel forecasting questions from global events reported in daily news, using a fully automated, careful curation recipe. We train the Qwen3

Shang-Qiang Ning, Xing-Yu Ren, Qing-Rui Wang, Yang Qi

Although classification for free-fermion topological superconductors (TSC) is established, systematically understanding the classification of 3D interacting TSCs remains difficult, especially those protected by crystalline symmetries like the 230 space groups. We build up a general framework for systematically classifying 3D interacting TSCs protected by cry

Pawel Cieslinski, Lukas Knips, Harald Weinfurter, Wieslaw Laskowski

Tomographic measurements are the standard tool for characterizing quantum states, yet they are usually regarded only as means for state reconstruction or fidelity measurement. Here, we show that the same Pauli-basis measurements (X, Y, Z) can be directly employed for the certification of nonlocality at no additional experimental cost. Our framework allows an

Dian Shao, Mingfei Shi, Like Liu

Recognizing fine-grained actions from temporally corrupted skeleton sequences remains a significant challenge, particularly in real-world scenarios where online pose estimation often yields substantial missing data. Existing methods often struggle to accurately recover temporal dynamics and fine-grained spatial structures, resulting in the loss of subtle mot