November 2025 arXiv papers — page 145

Po-Heng Chou, Chiapin Wang, Kuan-Hao Chen, Wei-Chen Hsiao

This paper investigates a lightweight deep reinforcement learning (DRL)-assisted weighting framework for CSI-free multi-satellite positioning in LEO constellations, where each visible satellite provides one serving beam (one pilot response) per epoch. A discrete-action Deep Q-Network (DQN) learns satellite weights directly from received pilot measurements an

Yuzhe Song, Debatri Chattopadhyay, Jarrod Hurley, Rainer Spurzem

Millisecond pulsars (MSPs) are neutron stars with spin periods as short as a few milliseconds, formed through mass accretion from companion stars. In the dense environments of globular clusters (GCs), MSPs are likely to originate through dynamically assembled interacting binaries. Over 300 MSPs have been detected in GCs to date, more than half of the known G

James C. Welch, Louis Jose, Timothy D. Tharp, Scott D. Baalrud

An important process for antimatter experiments is the cooling of particles in a Penning-Malmberg trap to experimentally useful temperatures. A non-neutral plasma of one species (e.g. antiprotons) can be collisionally cooled on another colder species (e.g. electrons). Modeling temperature relaxation in these devices is challenging from a plasma physics persp

Sanaa Sharma, Prakash Murali

Fault-tolerance is the future of quantum computing, ensuring error-corrected quantum computation that can be used for practical applications. Resource requirements for fault-tolerant quantum computing (FTQC) are daunting, and hence, compilation techniques must be designed to ensure resource efficiency. There is a growing need for compilation strategies tailo

Chunyan Li, Yutong Mao, Xiao Liu, Wenrui Hao

Understanding how biological systems evolve across space and time remains a fundamental challenge, particularly when dynamic processes vary substantially across individuals. We present a personalized graph-based dynamical modeling framework for characterizing spatiotemporal biological dynamics from longitudinal multimodal imaging data. The framework construc

Mattie Ji, Amauri H. Souza, Vikas Garg

Topological descriptors have been increasingly utilized for capturing multiscale structural information in relational data. In this work, we consider various filtrations on the (box) product of graphs and the effect on their outputs on the topological descriptors - the Euler characteristic (EC) and persistent homology (PH). In particular, we establish a comp

Daniel W. Collison, Archil Kobakhidze

We provide some additional comments on the long-lived discussions surrounding an effective action and potential plagued by a number of ambiguities. We reinforce the importance of an extra condition on the gauge-fixing function, namely the vanishing of its vacuum expectation value in the absence of external sources, when concluding gauge-independence of the e

CJ Sturgill, Manish Kumar, Nima Karimitari, Iva Milisavljevic

Wadsley-Roth (WR) niobates have emerged as high-rate anode materials that can combine rapid ionic diffusion with good electronic conductivity. WR compounds have been defect-enhanced by limited annealing, however, such materials often contain multiple types of defects. In particular, both Wadsley defects (variable block size) and transition metal disorder hav

Pavel Rytir, Phillip C. Burke, Christos Aravanis, Jiri Vala

We introduce the Mixed-Integer Quadratically Constrained Quadratic Programming framework for the quantum compilation problem and apply it in the context of topological quantum computing. In this setting, quantum gates are realized by sequences of elementary braids of quasiparticles with exotic fractional statistics in certain two-dimensional topological cond

Nivedina A. Sarma, David A. King, Xuefei Wu, Brett A. Helms

Membrane pores are implicated in several critical functions, including cell fusion and the transport of signaling molecules for intercellular communication. However, these structural features are often difficult to probe directly. Droplet interfacial bilayers offer a synthetic platform to study such membrane properties. We develop a theory that links size-se

Xunyang Hong, Yuetong Wu, Ying Chan, Sze Tung Li

Optimization of unconventional superconductivity involves a balance of interaction strengths. Precise determination of correlation strength across different material families is therefore important. Here, we present a combined X-ray absorption spectroscopy (XAS) and resonant inelastic X-ray scattering (RIXS) study of infinite-layer PrNiO$_2$ and SrCuO$_2$ th

Shubhangini Gupta, Prashant Sharma, Tamal Pramanick

In this study, we employ Euler's method and Richardson's extrapolation to solve a triple integral, which is then transformed into a third-order initial value problem. Our objective is to resolve the computational challenges associated with triple integration by transforming it into an initial value problem. Euler's method is the fundamental numer

Peter Lunkenheimer, Sebastian Emmert, Martin Wolf, Alois Loidl

In the present work, we examine the relevance and proper interpretation of broadband-dielectric and THz-spectroscopy data for the investigation of various types of biological matter. We provide an overview of the rich variety of different dynamic processes that can be detected by these experimental methods. Several experimental examples are discussed in deta

Michael A. Vladimir

FSampler is a training free, sampler agnostic execution layer that accelerates diffusion sampling by reducing the number of function evaluations (NFE). FSampler maintains a short history of denoising signals (epsilon) from recent real model calls and extrapolates the next epsilon using finite difference predictors at second order, third order, or fourth orde

Christoph Gaßner, Juliane Reisewitz, Jenna E. Forsyth, Kian Shaker

Human exposure to lead (Pb) is a global health concern, yet existing technologies for detecting lead in our environment remain prohibitively expensive for widespread deployment. Here we present a new concept towards lead screening using X-ray fluorescence (XRF) in an unconventional geometry we coin transmission XRF in which the sample is placed between the s

Junyeop Kim, Dongjin Lee, Woncheol Shin, Yeoulheon Seong

Quantum LiDAR offers noise resilience and stealth observation capabilities in low-light conditions. In prior demonstrations, the telescope pointing was raster-scanned, making the observation direction predictable from the pointing direction. However, while Quantum LiDAR can enable stealth observation, operational stealth is enhanced by inherently unpredictab

Christian Canete, Archil Kobakhidze

We entertain the possibility that the phenomena typically attributed to dark matter may have a fundamentally emergent nature, rather than arising from new particle degrees of freedom. To illustrate this idea, we consider a field-theoretic model of a three-form gauge field coupled to a cosmological fluid composed of ordinary matter and radiation. In the absen

Qiaochao Xiang, Xiaokang Li, Xiaodong Guo, Kamran Behnia

The phonon thermal Hall effect (THE) is a ubiquitous yet poorly understood phenomenon in insulators. Its microscopic origin remains debated, partly due to significant sample-dependent variations that hint at uncontrolled experimental parameters. Using SrTiO$_3$ as a model system, we identify disorder and uncontrolled strain as suppressors of a thermal Hall s

Shaoyun Bai, Boyu Zhang

We develop an equivariant Cerf theory for Morse functions on finite-dimensional manifolds with group actions, and adapt the technique to the infinite-dimensional setting to study the moduli space of perturbed flat $SU(n)$-connections. As a consequence, we prove the existence of perturbative $SU(n)$ Casson invariants on integer homology spheres for all $n\ge

Sarp Er, Marcello Meldi

A physics-based methodology for the determination of the localization function for the Ensemble Kalman Filter (EnKF) is proposed. The spatial features of such function evolve dynamically over time according to the relevant instantaneous flow features of the ensemble members with the objective, to reduce the computational cost of the Data Assimilation (DA) pr

Maria Y. Rodriguez, Seventy Hall, Pranav Sankhe, Melanie Sage

Scholars investigating ethical AI, especially in high stakes settings like child welfare, have arguably been seeking ways to embed notions of justice into the design of these critical technologies. These efforts often operationalize justice at the upper and lower bounds of its continuum, defining it in terms of progressiveness or reform. Before characterizin

Bibhushan Shakya

This paper explores various aspects and implications of the initial configuration of the Standard Model (SM) Higgs field at the beginning of our Universe. It is well known that the SM Higgs field features a deeper, more stable minimum at large field values. While it is possible that our Universe began and remained in the electroweak vacuum at all times, this

Eren Kurshan, Yuan Xie, Paul Franzon

AI systems have found a wide range of real-world applications in recent years. The adoption of edge artificial intelligence, embedding AI directly into edge devices, is rapidly growing. Despite the implementation of guardrails and safety mechanisms, security vulnerabilities and challenges have become increasingly prevalent in this domain, posing a significan

Xincheng Xu, Thilina Ranbaduge, Qing Wang, Thierry Rakotoarivelo

Differentially Private Stochastic Gradient Descent (DPSGD) is widely used to train deep neural networks with formal privacy guarantees. However, the addition of differential privacy (DP) often degrades model accuracy by introducing both noise and bias. Existing techniques typically address only one of these issues, as reducing DP noise can exacerbate clippin

Livia Grammatica, Alexei N. Skorobogatov, Yuan Yang

We study the Brauer group of an abelian variety A over an algebraically closed field of characteristic p focusing on the p-primary torsion, the key part of which is a certain quasi-algebraic unipotent group U_A. We determine its dimension and obtain a sharp upper bound for its p-exponent. The isogeny class of U_A is classified for abelian varieties A of dime

Shimiao Li, Aaron Tuor, Draguna Vrabie, Larry Pileggi

Learning to optimize (L2O) parametric approximations of AC optimal power flow (AC-OPF) solutions offers the potential for fast, reusable decision-making in real-time power system operations. However, the inherent nonconvexity of AC-OPF results in challenging optimization landscapes, and standard learning approaches often fail to converge to feasible, high-qu

Zihao Liu, Daniel Dias-da-Costa, Tommy Chan, Colin Coprani

This paper presents an output-only method to identify road roughness profiles from axle accelerations of a moving vehicle. A two degree of freedom half-car model is discretised with a zero-order hold and a backward-difference approximation of the roughness rate, which introduces both the current and previous roughness inputs into the observation equation. Th

Vladimir A. Baulin, Rudolf M. Füchslin, Achille Giacometti, Helmut Hauser

The design of intelligent materials often draws parallels with the complex adaptive behaviors of biological organisms, where robust functionality stems from sophisticated hierarchical organization and emergent long-distance coordination among a myriad local components. Current synthetic materials, despite integrating advanced sensors and actuators, predomina

Saeid Tafazzol, Ehsan Taheri

Successive convex programming (SCP) is a powerful class of direct optimization methods, known for its polynomial complexity and computational efficiency, making it particularly suitable for autonomous applications. Direct methods are also referred to as ``discretize-then-optimize'' with discretization being a fundamental solution step. A key step in all prac

Dmitrii Tsvetkov, Danilo Gomes Pires, Natalia Litchinitser

The stability of optical vortex structures in turbulent environments is critical for their applications in optical communication, quantum information, and structured light technologies. Although topological invariants, such as crossings and linking numbers, are fundamentally invariant, recent studies reveal that their observed values deteriorate considerably

Abdullah Al Helal, Jiri Lebl, Achinta Kumar Nandi

We study proper holomorphic maps of annuli in complex Euclidean spaces, that is, domains with $U(n)$ as the automorphism group. By the Hartogs phenomenon and a result of Forstneri\v{c}, such maps are always rational and extend to proper maps of balls. We first prove that a proper map of annuli from $n$ dimensions to $N$ dimensions where $N < \binom{n+1}{2}$

Jiaxun Guo, Manar Amayri, Nizar Bouguila, Xin Liu

Recent advances in rotation-invariant (RI) learning for 3D point clouds typically replace raw coordinates with handcrafted RI features to ensure robustness under arbitrary rotations. However, these approaches often suffer from the loss of global pose information, making them incapable of distinguishing geometrically similar but spatially distinct structures.

Nikunj Gupta, Ludwika Twardecka, James Zachary Hare, Jesse Milzman

In this paper, we propose capturing and utilizing \textit{Temporal Information through Graph-based Embeddings and Representations} or \textbf{TIGER} to enhance multi-agent reinforcement learning (MARL). We explicitly model how inter-agent coordination structures evolve over time. While most MARL approaches rely on static or per-step relational graphs, they o

Tomoki Koike, Elizabeth Qian

In the design and operation of complex dynamical systems, it is essential to ensure that all state trajectories of the dynamical system converge to a desired equilibrium within a guaranteed stability region. Yet, for many practical systems -- especially in aerospace -- this region cannot be determined a priori and is often challenging to compute. One of the

Jeffrey P. Thayer, Austin Coleman

This study applies a generalized vertical coordinate system approach alongside thermodynamic control volume analysis to explore the nuanced interpretations of energy transfer processes associated with vertical motion in the thermosphere. Using simulations from the TIEGCM V3.0 model, a key finding reveals that transforming vertical winds in height coordinates

A. Zanella, S. Belli, F. M. Valentino, A. Bolamperti

Molecular gas traces the fuel for star formation and the processes that regulate it. Observing its physical state (e.g. excitation) reveals when and why galaxies quench. We observed the CO(5-4) emission of 8 post-starburst (SB) galaxies at z~0.6-1.3. To our knowledge, this is the first time that high-J transitions are probed for quiescent galaxies beyond the

Luana Passos-Reis, Elisabete M. de Gouveia Dal Pino, Tarek Hassan, Santiago Pita

The Cherenkov Telescope Array Observatory (CTAO) will enable detailed studies of Active Galactic Nuclei (AGN) in the very-high-energy (VHE) regime, as the next-generation ground-based gamma-ray observatory, designed to enhance sensitivity and energy coverage (20 GeV -- 300 TeV) over current Imaging Atmospheric Cherenkov Telescopes (IACTs). In the context of

Guy Worthey, Tathagata Pal

Recent analysis of 2968 MaNGA early type galaxies has yielded two notable trends with velocity dispersion ($\sigma$) not previously discussed in the literature. First, Fe abundance rises with $\sigma$, but only until $\sigma\approx100$ km s$^{-1}$, after which it falls. This kink is reproduced by TNG100 simulations, implying that hierarchical merger processe

Zonglin Guo, Tony Givargis

Key-value stores are a fundamental class of NoSQL databases that offer a simple yet powerful model for data storage and retrieval, representing information as pairs of unique keys and associated values. Their minimal structure enables exceptionally fast access times, scalability, and flexibility in storing diverse data types, making them ideal for high-perfo

Yang You, Ufuk Çakır, Alex Schutz, Nick Hawes

The value function of a POMDP exhibits the piecewise-linear-convex (PWLC) property and can be represented as a finite set of hyperplanes, known as $\alpha$-vectors. Most state-of-the-art POMDP solvers (offline planners) follow the point-based value iteration scheme, which performs Bellman backups on $\alpha$-vectors at reachable belief points until convergen

Yi Yang, Xudong Wen, Lifan Wang, Dietrich Baade

The death of massive stars is triggered by an infall-induced bounce shock that disrupts the star. How such a shock is launched and propagates through the star is a decade-long puzzle. Some models assume that the shock can be reenergized by absorbing neutrinos, leading to highly aspherical explosions. Other models involve jet-powered shocks that lead to bipol

Wonbong Jang, Jonathan Tremblay, Lourdes Agapito

Generating novel views of a natural scene, e.g., every-day scenes both indoors and outdoors, from a single view is an under-explored problem, even though it is an organic extension to the object-centric novel view synthesis. Existing diffusion-based approaches focus rather on small camera movements in real scenes or only consider unnatural object-centric sce

Nelson Durrant, Braden Meyers, Matthew McMurray, Clayton Smith

Real-world underwater testing for multi-agent autonomy presents substantial financial and engineering challenges. In this work, we introduce the Configurable Underwater Group of Autonomous Robots (CoUGARs) as a low-cost, configurable autonomous-underwater-vehicle (AUV) platform for multi-agent autonomy research. The base design costs less than $3,000 USD (as

Ismail Lamaakal, Chaymae Yahyati, Yassine Maleh, Khalid El Makkaoui

We present BayesQ, an uncertainty-guided post-training quantization framework that is the first to optimize quantization under the posterior expected loss. BayesQ fits a lightweight Gaussian posterior over weights (diagonal Laplace by default; optional K-FAC/low-rank), whitens by the posterior covariance, designs codebooks to minimize posterior-expected dist

Diego Fernando Fonseca, Leonardo Castañeda, Luz Ángela García

Cosmological perturbation theory provides the fundamental framework for describing the evolution of the matter-energy density field in an expanding Universe and serves as the basis for understanding the formation of large-scale structures within the $\Lambda$CDM paradigm. We present an analytical approach to describe the evolution of fluctuations in a mixed

Andrew Rothstein, Hiro Joseph Farre-Kaga, Jalal Butt, Ricardo Shousha

We present the design and application of a general algorithm for Prediction And Control using MAchiNe learning (PACMAN) in DIII-D. Machine learing (ML)-based predictors and controllers have shown great promise in achieving regimes in which traditional controllers fail, such as tearing mode free scenarios, ELM-free scenarios and stable advanced tokamak condit

Nicolas Bouchot

We consider the simple random walk conditioned to stay forever in a finite domain $D_N \subset \mathbb{Z}^d, d \geq 3$ of typical size $N$. This confined walk is a random walk on the conductances given by the first eigenvector of the Laplacian on $D_N$. On inner sets of $D_N$, the trace of this confined walk can be approximated by tilted random interlacement

M. Volz, C. C. Espaillat, C. V. Pittman, S. L. Grant

We present JWST-MIRI Medium Resolution Spectrometer (MRS) observations of the Classical T Tauri stars GM Aur and RX J1615.3-3255 (J1615), both hosting transitional disks. Despite their similar stellar and disk properties, the two systems differ strikingly in their carbon-bearing molecular emission. Using local thermodynamic equilibrium (LTE) slab models to a

A. Leonau, S. Chuchurka, V. Sukharnikov, A. Benediktovitch

We critically explore the applicability of a recently proposed framework to sample the quantum dynamics of a many-body quantum system interacting with light by stochastic trajectories, applying it to the closed and open Tavis-Cummings model (TCM). The stochastic differential equations (SDEs) sample the positive P phase-space representation by analog complex-

Steven D Miller

We present a derivation of the mechanics of isothermal gas spheres directly from the Vlasov--Poisson equation. By extremising the Boltzmann entropy, we obtain the Maxwell--Boltzmann distribution for a self-gravitating isothermal Newtonian gas, which is a stationary solution of the Vlasov--Poisson system. From this distribution, the corresponding Poisson--Bol

Farah Binta Haque, Md Yasin, Shishir Saha, Md Shoaib Akhter Rafi

Despite the growing progress in Natural Language Inference (NLI) research, resources for the Bengali language remain extremely limited. Existing Bengali NLI datasets exhibit several inconsistencies, including annotation errors, ambiguous sentence pairs, and inadequate linguistic diversity, which hinder effective model training and evaluation. To address thes

Setareh Hasanabadi, Maryam Cheraghi, Hossein Behnam Manesh, Mohadeseh Bayat

This pilot study compares per-lesion radiomics features of [68Ga]-DOTA FAPI-46 and [18F]-FDG PET/CT in non-small cell lung cancer (NSCLC) to explore complementary insights into intratumoral heterogeneity beyond conventional SUV metrics, aiming to enhance lesion characterization and clinical decision-making. A total of 28 PET/CT scans (14 [18F]-FDG and 14 [68

Youngkyu Lee, Shanqing Liu, Jerome Darbon, George Em Karniadakis

We propose a novel neural preconditioned Newton (NP-Newton) method for solving parametric nonlinear systems of equations. To overcome the stagnation or instability of Newton iterations caused by unbalanced nonlinearities, we introduce a fixed-point neural operator (FPNO) that learns the direct mapping from the current iterate to the solution by emulating fix

Jingjie He, Weijie Liang, Zihan Shan, Matthew Caesar

Adversarial attacks expose a fundamental vulnerability in modern deep vision models by exploiting their dependence on dense, pixel-level representations that are highly sensitive to imperceptible perturbations. Traditional defense strategies typically operate within this fragile pixel domain, lacking mechanisms to incorporate inherently robust visual feature

Abu Taib Mohammed Shahjahan, A. Ben Hamza

Graph convolutional network (GCN)-based methods have shown strong performance in 3D human pose estimation by leveraging the natural graph structure of the human skeleton. However, their local receptive field limits their ability to capture long-range dependencies essential for handling occlusions and depth ambiguities. They also exhibit spectral bias, which

Hanchen David Wang, Siwoo Bae, Zirong Chen, Meiyi Ma

Artificial intelligence systems in critical fields like autonomous driving and medical imaging analysis often continually learn new tasks using a shared stream of input data. For instance, after learning to detect traffic signs, a model may later need to learn to classify traffic lights or different types of vehicles using the same camera feed. This scenario

Matheus Vinícius Barreto de Farias, Mario de Castro

This study investigates the impact of adding noise to the training set classes in classification tasks using the BCOPS algorithm (Balanced and Conformal Optimized Prediction Sets), proposed by Guan & Tibshirani (2022). The BCOPS algorithm is an application of conformal prediction combined with a machine learning method to construct prediction sets such that

Juan José Montellano-Ballesteros, Christian Rubio-Montiel

An acyclic coloring of a digraph that maximizes the number of colors such that each color class has a vertex pointing to all other classes and a vertex pointing to it from all other classes is known as the dib-chromatic number of a digraph. In this paper, we answer the question about the existence of the dib-chromatic number and study the dib-chromatic numbe

Varada Khanna, Nilay Bhatt, Ikgyu Shin, Sule Tinaz

Understanding how individuals with Parkinson's disease (PD) describe cognitive experiences in their daily lives can offer valuable insights into disease-related cognitive and emotional changes. However, extracting such information from unstructured patient narratives is challenging due to the subtle, overlapping nature of cognitive constructs. This study dev

Matthew Viens, J. Kyle Skolfield, William E. Hart, Michael Ferris

Power systems modeling and planning has long leveraged mathematical programming for its ability to provide optimality and feasibility guarantees. One feature that has been recognized in the optimization literature since the 1970s is the existence and meaning of multiple exact optimal and near-optimal solutions, which we call alternative solutions. In power s

Sebanti Chattopadhyay, Carys Chase-Mayoral, Nathan Keim

The storage and retrieval of mechanical imprints from past perturbations is a central theme in soft matter physics. Here we study this effect in the partial peeling of an ordinary adhesive tape, which leaves a line of strong adhesion at the stopping point. We show how this behavior can be used to mechanically store and retrieve the amplitudes of successive p

Sixiang Zhou, Nan Deng, Krzysiek Rzadca, Xiaojun Lin

Modern warehouse-scale datacenters commonly collocate multiple jobs on shared machines to improve resource utilization. However, such collocation often leads to performance interference caused by antagonistic jobs that overconsume shared resources. Existing antagonist-detection approaches either rely on offline profiling, which is costly and unscalable, or u

Maxime Fajgenblat, Marc Herremans, Pieter Vanormelingen, Kristijn Swinnen

For many taxonomic groups, online biodiversity portals used by naturalists and citizen scientists constitute the primary source of distributional information. Over the last decade, site-occupancy models have been advanced as a promising framework to analyse such loosely structured, opportunistically collected datasets. Current approaches often ignore importa

Mahsa Derakhshan, Mohammad Roghani, Mohammad Saneian, Tao Yu

We provide a simple combinatorial analysis of the Ranking algorithm, originally introduced in the seminal work by Karp, Vazirani, and Vazirani [KVV90], demonstrating that it achieves a $(1/2 + c)$-approximate matching for general graphs for $c \geq 0.005$.

A. Lazarian, D. Pogosyan, Y. Hu

MHD turbulence driven at velocities higher than the Alfv\'en velocity, i.e., super-Alfv\'enic turbulence, is widely spread in astrophysical environments, including galaxy clusters and molecular clouds. For statistical studies of such turbulence, we explore the utility of the polarization angle structure functions $D^\phi(R)= \left\langle\sin^2(\phi_1-\phi_2)

Mark D. Groves, Dag Nilsson, Leon Schütz

We discuss axisymmetric solitary waves on the surface of an otherwise cylindrical ferrofluid jet surrounding a stationary metal rod. The ferrofluid, which is governed by a general (nonlinear) magnetisation law, is subject to an azimuthal magnetic field generated by an electric current flowing along the rod. We treat the governing equations using a modificati

Manan Suri, Puneet Mathur, Nedim Lipka, Franck Dernoncourt

LLM agents with tool-calling capabilities often fail when user instructions are ambiguous or incomplete, leading to incorrect invocations and task failures. Existing approaches operate in unstructured language spaces, generating clarifying questions through prompting strategies that lack principled criteria for determining which questions to ask and when to

Jiaxing Zhao, Joerg Aichelin, Pol Bernard Gossiaux, Klaus Werner

The different QCD processes, which can produce a heavy quark-antiquark ($Q\bar Q$) pair, induce different correlations between the heavy quarks. Employing the EPOS4HQ event generator we study the consequences of these correlations and compare the calculation with experimental results on open and hidden heavy flavour mesons, measured in proton-proton (pp) col

Boya Liu, Weinan Wang

We show that a partial Dirichlet-to-Neumann map, where the measurement set is arbitrarily small, uniquely determines the time-dependent nonlinearity of order three or higher in a semi-linear wave equation up to natural obstructions on a Lorentzian manifold with boundary. In particular, we do not impose any geometric or size restrictions on the measurement se

David Loeffler, Sarah Livia Zerbes

We develop a machine for bounding Selmer groups of Galois representations via Euler systems in "non-ordinary" settings, using Pottharst's definition of Selmer groups via Robba-ring $(\varphi, \Gamma)$-modules. Our approach relies on Sweeting's interpretation of Kolyvagin derivative classes via non-principal ultrafilters. We apply these results to prove new c

Shravan Saoji

We study the higher Nash blow-ups introduced by T. Yasuda and investigate the higher version of the classical Nobile's theorem. In particular, we give a characteristic free proof of the higher Nobile's theorem for the graded case. We also give a proof for the 2nd order Nash blow-ups in characteristic zero.

Rocco A. Servedio

This survey paper gives an overview of various known results on learning classes of Boolean functions in Valiant's Probably Approximately Correct (PAC) learning model and its commonly studied variants.

Paula Moraga Baez, Joel H. Kastner, Jesse Bublitz, Javier Alcolea

We present results from a program of Atacama Large Millimeter Array (ALMA) 1.3 mm (Band 6) molecular line mapping of a sample of nearby, bipolar/pinched-waist, molecule-rich PNe (NGC 6302, Hubble 5, NGC 2440, NGC 6445, NGC 2899, and NGC 2818). Maps of $^{12}$CO(2$-$1) and $^{13}$CO(2$-$1) emission as well as emission lines of HCN, HNC, HCO$^+$, CN, and CS $-

Junchen Liu, Yi Sheng

As a widely adopted model compression technique, model pruning has demonstrated strong effectiveness across various architectures. However, we observe that when sparsity exceeds a certain threshold, both iterative and one-shot pruning methods lead to a steep decline in model performance. This rapid degradation limits the achievable compression ratio and prev

David Pfau

The bias-variance decomposition is a central result in statistics and machine learning, but is typically presented only for the squared error. We present a generalization of the bias-variance decomposition where the prediction error is a Bregman divergence, which is relevant to maximum likelihood estimation with exponential families. While the result is alre

Ridvan Bari Urcosta

This paper introduces the "Singularity Warfare" concept, arguing that the accelerating pace of technological revolution, driven by artificial intelligence and quantum mechanics, is fundamentally reshaping the nature of conflict. Moving beyond traditional "Newtonian" warfare and current military doctrines, this framework posits that future battlefields will b

Gil Cohen, Dean Doron, Noam Goldgraber, Tomer Manket

One of the oldest problems in coding theory is to match the Gilbert-Varshamov bound with explicit binary codes. Over larger-yet still constant-sized-fields, algebraic-geometry codes are known to beat the GV bound. In this work, we leverage this phenomenon by taking traces of AG codes. Our hope is that the margin by which AG codes exceed the GV bound will wit

Yongqiang Liu, Laurentiu Maxim, Botong Wang

We show that the Betti numbers of a local system on the complement of an essential complex hyperplane arrangement are maximized precisely when the local system is constant. This result answers positively a recent question of Yoshinaga and the first author.

John M. Hitchcock, Adewale Sekoni, Hadi Shafei

Classical results of Bennett and Gill (1981) show that with probability 1, $P^A \neq NP^A$ relative to a random oracle $A$, and with probability 1, $P^\pi \neq NP^\pi \cap coNP^\pi$ relative to a random permutation $\pi$. Whether $P^A = NP^A \cap coNP^A$ holds relative to a random oracle $A$ remains open. While the random oracle separation has been extended

M. A. Gameiro

This study addresses the challenge of integrating complex, high-dimensional deep semantic features with simple, interpretable structural cues for lyrical content classification. We introduce a novel Synergistic Fusion Layer (SFL) architecture, a deep learning model utilizing a gated mechanism to modulate Sentence-BERT embeddings (Fdeep) using low-dimensional

Anais Galdin, Jesse Silbert

Large language models (LLMs) like ChatGPT have significantly lowered the cost of producing written content. This paper studies how LLMs, through lowering writing costs, disrupt markets that traditionally relied on writing as a costly signal of quality (e.g., job applications, college essays). Using data from Freelancer.com, a major digital labor platform, we

P. Darc, Clecio R. Bom, Charles Kilpatrick, Bernardo M. O. Fraga

Gravitational wave sources with electromagnetic counterparts have highlighted the need for predictive, interpretable models linking the parameters of compact binary systems to post-merger remnants and mass outflows. In this work, we explore AI-driven symbolic regression (SR) frameworks to derive updated analytical relations for disk ejecta mass in binary neu

Hua Lin, Peng-Jie Wong

A famous conjecture of Keating and Snaith asserts that central values of $L$-functions in a given family admit a log-normal distribution with a prescribed mean and variance depending on the symmetry type of the family. Based on a recent work of Radziwill and Soundararajan, we obtain a conditional lower bound towards Keating-Snaith's conjecture for a "thin" f

Sambit Das, Bikash Kanungo, Arghadwip Paul, Vikram Gavini

The Hohenberg-Kohn (HK) theorem -- the bedrock of density functional theory (DFT) -- establishes a universal map from the external potential to the energy. It also relates the electron density and atomic forces to the variation of the energy with the external potential. But the HK map is rarely utilized in atomistics, wherein interatomic potentials are defin

Valentino F. Foit, David W. Hogg, Soledad Villar

Many machine learning tasks in the natural sciences are precisely equivariant to particular symmetries. Nonetheless, equivariant methods are often not employed, perhaps because training is perceived to be challenging, or the symmetry is expected to be learned, or equivariant implementations are seen as hard to build. Group averaging is an available technique

V. I. Bogachev, S. V. Shaposhnikov, D. V. Shatilovich

We obtain sufficient conditions for the uniqueness of a probability solution to the stationary Kolmogorov equation with a degenerate diffusion matrix. We employ the method of doubling variables known in stochastic analysis directly to the Kolmogorov equation.

Mohammadali Aligholi, Laurentiu Maxim, Joerg Schuermann

We give an overview of recent developments around a characteristic class version of the Hodge index theorem for singular complex algebraic varieties. This was formulated by Brasselet-Schuermann-Yokura as a conjecture expressing the Goresky-MacPherson homology L-classes in terms of suitable Hodge-theoretic L-classes. Along the way, we clarify the relationship

Richard Cheng, Peter Werner, Carolyn Matl

High degree-of-freedom dual-arm robots are becoming increasingly common due to their morphology enabling them to operate effectively in human environments. However, motion planning in real-time within unknown, changing environments remains a challenge for such robots due to the high dimensionality of the configuration space and the complex collision-avoidanc

Michael Wilson

We identify the $r^{-3}$ curvature-decay rate as a universal geometric threshold separating compact from non-compact perturbations of Laplace-type operators on asymptotically flat manifolds. For the coupled Einstein--Maxwell system, we prove that the linearized operator $\mathcal{L}$ is essentially self-adjoint and that curvature and field strengths decaying

Stefanos Georgiadis, Stefano Spirito

In this paper, we consider a family of one-dimensional fourth order evolution equations arising as gradient flows of the Korteweg energy, i.e. the $L^2$-norm of the first derivative of some power of the density. This family of equations generalizes the Quantum-Drift-Diffusion equation and the Thin-Film equation. We prove the global-in-time existence of {\em

Sergi Liesegang, Stefano Buzzi

This paper addresses the power control design for a cell-free massive MIMO (CF-mMIMO) system that performs integrated sensing and communications (ISAC). Specifically, the case where many access points are deployed to simultaneously communicate with mobile users and monitor the surrounding environment at the same time-frequency slot is considered. On top of t

Rafael Bailo, Julie Binard, Pierre Degond, Pascal Noble

We present and study a Particle method for the stationary solutions of a class of transport equations. This method is inspired by non-stationary Particle methods, the time variable being replaced by one spatial variable. Particles trajectories are computed using the ``time-dependent'' equations, and then the approximation is based on a quadrature method usin

Xiang-Yu Wu, Charles Gale, Sangyong Jeon, Jean-François Paquet

We perform a study of electromagnetic radiation in heavy-ion collisions at Relativistic Heavy Ion Collider (RHIC) Beam Energy Scan (BES) and SPS energies using the iEBE-MUSIC framework, which includes 3D dynamical Monte Carlo Glauber initial conditions, MUSIC (3+1)D viscous relativistic hydrodynamics, and the UrQMD hadronic afterburner. The multistage modeli

Myeonghun Yu, Kean Ming Tan, Huixia Judy Wang, Wen-Xin Zhou

Expected shortfall (ES), also known as conditional value-at-risk, is a widely recognized risk measure that complements value-at-risk by capturing tail-related risks more effectively. Compared with quantile regression, which has been extensively developed and applied across disciplines, ES regression remains in its early stage, partly because the traditional

Vince Kurtz, Alejandro Castro

State-of-the-art robotics simulators operate in discrete time. This requires users to choose a time step, which is both critical and challenging: large steps can produce non-physical artifacts, while small steps force the simulation to run slowly. Continuous-time error-controlled integration avoids such issues by automatically adjusting the time step to achi

J. Boynewicz, C. A. Sackett

Quantum reflection occurs when ultra-cold atoms are incident on a material surface with sufficiently low velocity. The reflecting matter wave can interfere with the incident wave to form a detectable pattern, and this pattern contains information about atom-surface interactions at micrometer scales. We discuss how such an interferometer could be used to prob

Anuab Sen, Mir Sayeed Mohammad, Saibal Mukhopadhyay

We introduce SSMRadNet, the first multi-scale State Space Model (SSM) based detector for Frequency Modulated Continuous Wave (FMCW) radar that sequentially processes raw ADC samples through two SSMs. One SSM learns a chirp-wise feature by sequentially processing samples from all receiver channels within one chirp, and a second SSM learns a representation of

M. Dévora-Pajares, F. J. Pozuelos, J. C. Suárez, M. González-Penedo

Context. As the number of detected transiting exoplanet candidates continues to grow, the need for robust and scalable automated tools to prioritize or validate them has become increasingly critical. Among the most promising solutions, deep learning models offer the ability to interpret complex diagnostic metrics traditionally used in the vetting process. Ai

Connor Hanley, Eilene Tomkins-Flanaganm, Mary Alexandria Kelly

Using Frequency-domain Holographic Reduced Representations (FHRRs), we extend a Vector-Symbolic Architecture (VSA) encoding of Lisp 1.5 with primitives for arithmetic operations using Residue Hyperdimensional Computing (RHC). Encoding a Turing-complete syntax over a high-dimensional vector space increases the expressivity of neural network states, enabling n

Benjamin Cellini, Burak Boyacioglu, Austin Lopez, Floris van Breugel

From organisms to machines, autonomous systems rely on measured sensory cues to estimate unknown information about themselves or their environment. For nonlinear systems, strategic sensor motion can be leveraged to extract otherwise inaccessible information. This principle, known as active sensing, is widespread in biology yet difficult to study, and remains